available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip...

23
L) -rf-71 DCI9s: 0904o~/ ppocesse/ f 3 Y 5Y1 T" I! "VMade available under NASA sponsorShip in the. interest of early and wide dis- 7.6- 1 0. 46 8. semination of Earth Resources Survey P'ogram information and without liability OA" - for any use Made thereof." Title of Investigation: 28990, Investigation of Environmental Change Pattern in Japan. Principal Investigator: Dr. Takakazu Maruyasu Science University of Tokyo Noda City, Chiba-Ken, 278, Japan Co 'Investigator: Hiroaki Ochiai Toba Merchant Marine College Toba City, Mie-Ken, Japan Date of Submission: Jdn';.:..30, 1976 Quarterly Progress Report for Period April-June 1976 Oigint l ph tograply may be purchased from: EROS Data Center 10th and Dakota Avenue Sioux 5ails, SO 57198 (E76-10468) INVESTIGATION OF ENVIRONMENTAL N76-31616 CHANGE PATTERN IN JAPAN. APPLICATION OF HCS ,ro LANDSAT-2 DATA TO ENVIRONMENTAL STUDIES IN COASTAL ZONE Quarterly Progress Report, Unclas Apr. -_Jun. 1976[_Science Univ, of Tnky 3/_43- 00468 https://ntrs.nasa.gov/search.jsp?R=19760024528 2020-05-25T12:13:26+00:00Z

Transcript of available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip...

Page 1: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

L) -rf-71 DCI9s 0904o~ ppocesse f3Y 5Y1 T I

VMade available under NASA sponsorShip in the interest of early and wide dis- 76- 1 0 46 8 semination of Earth Resources Survey Pogram information and without liability OAshyfor any use Made thereof

Title of Investigation 28990 Investigation of Environmental Change Pattern

in Japan

Principal Investigator Dr Takakazu Maruyasu

Science University of Tokyo

Noda City Chiba-Ken 278 Japan

Co Investigator Hiroaki Ochiai Toba Merchant Marine College

Toba City Mie-Ken Japan

Date of Submission Jdn30 1976

Quarterly Progress Report for Period

April-June 1976

Oigintl ph tograply may be purchased from EROS Data Center 10th and Dakota Avenue Sioux 5ails SO 57198

(E76-10468) INVESTIGATION OF ENVIRONMENTAL N76-31616 CHANGE PATTERN IN JAPAN APPLICATION OF HCS ro LANDSAT-2 DATA TO ENVIRONMENTAL STUDIES IN COASTAL ZONE Quarterly Progress Report Unclas Apr -_Jun 1976[_Science Univ of Tnky 3_43- 00468

httpsntrsnasagovsearchjspR=19760024528 2020-05-25T121326+0000Z

2

Application of LANDSAT-2 Data to SIS Code 9026

Environmental Studies in Coastal Zone Investigation 28990 No

Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Red tide in Seto Inland Sea

Seto Inland Sea especially eastern half of it is noted as

one of the most polluted inland water area in Japan and we have

experienced red tide through the year in everywhere According

to the report announced by The Branch Office of Fishery Agency in

Kobe the total occurrence of red tide for atyearinKSeto Inland

Seais inclined to increasihg yearbyyear and it exceeded two

hundred times in recent year as shown in Table 1

Table 1 Total Occurrence of Red Tide in Seto Inland Sea

Year 1967 1968 1969 1970 1971 1972 1973 1974

Total No 48 61 67 79 136 164 210 298

As shown in Figure 1 almost area of Osaka Bay Sea of

Harima and Sea of Bingo hith consist i6 Lstern half of Setaunland

Sea were suffered by red tide in 1973 and 1974(data of 1975 is

not yet received) In 1974 we have experienced the red tide in

winter once we have not experienced

Monitoring of red tide by LANDSAT data

An MSS-4 imagery acquired on December 30 1975 was used for

the purpose of investigation In late December of 1975 and early

January of 1976several red tide consisted by Skeltonema were

reported by fishing boat in coastal area of Sea of Harima along

the northern coast But no report was accepted which tells the

occurrence of red tide in central area of Sea of Harima in these

periods In Figure 2 several patterns indicated by black arrows

were estimated as red tide area depend on the experience of

ANDAT-1investigation and airborn remote sensing The reason

why the red tide detected in centralarea of Sea of Harima by LANDSAT imagery was not reported by fishing boat was estimated

that almost fishing boat were not at sea for fishing as year end

and new year holidays Monitoring of red tide by LANDSAT lik

this case is supposed very effective in Seto Inland Sea in future

I

3973 uimejii

C H U G 0 K U Kob~e

ea of O - kay a a Earima Os k

1973

Kob

- 1 VKO U Himeji-

1 a moa Sea I Osa a

1979

Figure 1 Map ofeatr fof Set Inan Sea Shde

area means the boundary of red tide were sighted

through the year

2

ORIGINAL PACE IS Ok QUALITY

Figure 2 MSS-4 imagery of LAXDSAT-2 acquired over Seto Inland Sea December 30 1975

3

3 Monitoring of sedimentation

Along the southern coast of Hokkaido between Tomakomai and

Urakawa typical expanding pattern of sediment was detected in

MSS-4 imagery As indicated in authors previous report()

MSS-4 imagery is very effective to detect the dritribution of

sediment especially suspended sediment from the river

In Figure 3 expanding pattern from the mouth of Saru River

extended to southwest direction more than 15 Km long Saru River

is noted as polluted water with suspended sediment So the

density of suspended sediment is more concentrative compared with

surrounding area

According to the report issued by Hokkaido Prefecture Saru

River was named depend on its characteristic Namely the river

water contains so much suspended sediment on normal condition

the river was named as River which flows sand in Japanese

S River

iver effluent

Figure 3 MSS-4 Imagery detected the distribution

of sediment June 11 1975

Except the expanding pattern from the mouth of Saru River

the distribution of sediment along the coast was directed to

eastward caused by shore current in this area So stand on the

distribution pattern of sediment shore Current-would be

recognized easily in LANDSAT MSS data obtained at lowest condition

of sea level

4 REpPnUfv Y OF TIM oampGE B P0A

Along the northeast coast of Hokkaidobetween Monbetsu and Abashiri the distribution pattern of sediment was recognized as

the index of shore current in this areaas shown in Figure 4

At the outside of Lake Saroma a round-type pattern indicated by black arrow was detected and it was estimated as sediment

bulges out to the sea through sandy shoals which consist the

outside bank of Lake Saroma

Sediment bul

Monbetsu

Lake Saroma

Figure 4 Sediment bulge detected in MSS-4 imagery June 11 1975

Reference

(1) Hiroaki OchiaiMultidisciplinary Application of LANDSAT-2

Data to Marine Environment in Central Japan Progress

report of LANDSAT-2 investigation

5

288020 RECEIVED BY

NASA STI FACILITY Mdei awallat under NASA swsOCmt DATE- S2 Inthe interest of early ant wide 41s D A NO seminition of Earth Resources SunY DOAF NO

PROCE DProgram intormation and wtthout liability NASA STI FACILITYfor any L-e Made th lreot

QESA-SS ] AI

Title of Investigation 28990 Investigation of Environmental Change Pattern in Japan C76 - 1 O 4 6-

Principal Investigator Dr Takakazu Maruyasu 761 Science University of Tokyo

Noda City Chiba-Ken 278 Japan

Co-Investigator Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mlie-Ken Japan

Date of Submission jiine 30 1976

Quarterly Progress Report for Period

April -June 1976

0rial 0hatography my be cwctund frets EROS Data Center 10th and Dakota Avenue Sioux Falls SD 571 A

REFRDU BYl NATIONAL TECHNICAL INFORMAiON SVICE

SDEPARTMKT OFC SPRINWFIILDVA226

K

Digital Analysis of LANDSAT-2 MSS Dntn STS Codpe 902o in Costal Zone in Central Japan Investigation 28990

No Hiroaki Ochiai July 28 1976

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Introduction

For the purpose of attempt to classify the field informations

revealed by LANDSAT-2 the author tried digital analysis of multishy

spectral scanner data using LARSYS package Although LARSYS was

well known as developed for agriculture remote sensing in first

step depend on the ajustment of the software recently it was

clarified as very effective in various part of remote sensing As

shown in Figure 1 the coastal area in Kii Peninsula Was not

well classified by Photo-interpretation

2 MIS Tape

Before the digital analysis the CCT Tape(9 trucks 1600 BPI) delivered from NASA was reformatted to MIS(Multispectral Image

Storage) Tape which include several Runs data-(l) identification

record(2) data records(3) end-of-file record

3 Analysis flow and result

Analysis flow by un-supervised technique was showed in Figure 2 In first step of analysis the quality check of the data was

performed in Histogram Namely we cofldidentified the relative

radiancein each wavelength

In second step several training fields showed in Figure 3

were settled in Picture Print which shows the distribution of

resolution by Lines and Columns Line means scan lines and Column

means samples within a scan line of the data In this case the

Line interval and Column interval were reduced for data compression each two intervals Relative radiance scale of 0 to 255 were

displayed in Picture Print

I

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

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2014

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1602 1281

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Figure 4 Clustering information

5

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Figure 5 2 Field informations(continued)

7

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Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

_q

Page 2: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

2

Application of LANDSAT-2 Data to SIS Code 9026

Environmental Studies in Coastal Zone Investigation 28990 No

Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Red tide in Seto Inland Sea

Seto Inland Sea especially eastern half of it is noted as

one of the most polluted inland water area in Japan and we have

experienced red tide through the year in everywhere According

to the report announced by The Branch Office of Fishery Agency in

Kobe the total occurrence of red tide for atyearinKSeto Inland

Seais inclined to increasihg yearbyyear and it exceeded two

hundred times in recent year as shown in Table 1

Table 1 Total Occurrence of Red Tide in Seto Inland Sea

Year 1967 1968 1969 1970 1971 1972 1973 1974

Total No 48 61 67 79 136 164 210 298

As shown in Figure 1 almost area of Osaka Bay Sea of

Harima and Sea of Bingo hith consist i6 Lstern half of Setaunland

Sea were suffered by red tide in 1973 and 1974(data of 1975 is

not yet received) In 1974 we have experienced the red tide in

winter once we have not experienced

Monitoring of red tide by LANDSAT data

An MSS-4 imagery acquired on December 30 1975 was used for

the purpose of investigation In late December of 1975 and early

January of 1976several red tide consisted by Skeltonema were

reported by fishing boat in coastal area of Sea of Harima along

the northern coast But no report was accepted which tells the

occurrence of red tide in central area of Sea of Harima in these

periods In Figure 2 several patterns indicated by black arrows

were estimated as red tide area depend on the experience of

ANDAT-1investigation and airborn remote sensing The reason

why the red tide detected in centralarea of Sea of Harima by LANDSAT imagery was not reported by fishing boat was estimated

that almost fishing boat were not at sea for fishing as year end

and new year holidays Monitoring of red tide by LANDSAT lik

this case is supposed very effective in Seto Inland Sea in future

I

3973 uimejii

C H U G 0 K U Kob~e

ea of O - kay a a Earima Os k

1973

Kob

- 1 VKO U Himeji-

1 a moa Sea I Osa a

1979

Figure 1 Map ofeatr fof Set Inan Sea Shde

area means the boundary of red tide were sighted

through the year

2

ORIGINAL PACE IS Ok QUALITY

Figure 2 MSS-4 imagery of LAXDSAT-2 acquired over Seto Inland Sea December 30 1975

3

3 Monitoring of sedimentation

Along the southern coast of Hokkaido between Tomakomai and

Urakawa typical expanding pattern of sediment was detected in

MSS-4 imagery As indicated in authors previous report()

MSS-4 imagery is very effective to detect the dritribution of

sediment especially suspended sediment from the river

In Figure 3 expanding pattern from the mouth of Saru River

extended to southwest direction more than 15 Km long Saru River

is noted as polluted water with suspended sediment So the

density of suspended sediment is more concentrative compared with

surrounding area

According to the report issued by Hokkaido Prefecture Saru

River was named depend on its characteristic Namely the river

water contains so much suspended sediment on normal condition

the river was named as River which flows sand in Japanese

S River

iver effluent

Figure 3 MSS-4 Imagery detected the distribution

of sediment June 11 1975

Except the expanding pattern from the mouth of Saru River

the distribution of sediment along the coast was directed to

eastward caused by shore current in this area So stand on the

distribution pattern of sediment shore Current-would be

recognized easily in LANDSAT MSS data obtained at lowest condition

of sea level

4 REpPnUfv Y OF TIM oampGE B P0A

Along the northeast coast of Hokkaidobetween Monbetsu and Abashiri the distribution pattern of sediment was recognized as

the index of shore current in this areaas shown in Figure 4

At the outside of Lake Saroma a round-type pattern indicated by black arrow was detected and it was estimated as sediment

bulges out to the sea through sandy shoals which consist the

outside bank of Lake Saroma

Sediment bul

Monbetsu

Lake Saroma

Figure 4 Sediment bulge detected in MSS-4 imagery June 11 1975

Reference

(1) Hiroaki OchiaiMultidisciplinary Application of LANDSAT-2

Data to Marine Environment in Central Japan Progress

report of LANDSAT-2 investigation

5

288020 RECEIVED BY

NASA STI FACILITY Mdei awallat under NASA swsOCmt DATE- S2 Inthe interest of early ant wide 41s D A NO seminition of Earth Resources SunY DOAF NO

PROCE DProgram intormation and wtthout liability NASA STI FACILITYfor any L-e Made th lreot

QESA-SS ] AI

Title of Investigation 28990 Investigation of Environmental Change Pattern in Japan C76 - 1 O 4 6-

Principal Investigator Dr Takakazu Maruyasu 761 Science University of Tokyo

Noda City Chiba-Ken 278 Japan

Co-Investigator Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mlie-Ken Japan

Date of Submission jiine 30 1976

Quarterly Progress Report for Period

April -June 1976

0rial 0hatography my be cwctund frets EROS Data Center 10th and Dakota Avenue Sioux Falls SD 571 A

REFRDU BYl NATIONAL TECHNICAL INFORMAiON SVICE

SDEPARTMKT OFC SPRINWFIILDVA226

K

Digital Analysis of LANDSAT-2 MSS Dntn STS Codpe 902o in Costal Zone in Central Japan Investigation 28990

No Hiroaki Ochiai July 28 1976

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Introduction

For the purpose of attempt to classify the field informations

revealed by LANDSAT-2 the author tried digital analysis of multishy

spectral scanner data using LARSYS package Although LARSYS was

well known as developed for agriculture remote sensing in first

step depend on the ajustment of the software recently it was

clarified as very effective in various part of remote sensing As

shown in Figure 1 the coastal area in Kii Peninsula Was not

well classified by Photo-interpretation

2 MIS Tape

Before the digital analysis the CCT Tape(9 trucks 1600 BPI) delivered from NASA was reformatted to MIS(Multispectral Image

Storage) Tape which include several Runs data-(l) identification

record(2) data records(3) end-of-file record

3 Analysis flow and result

Analysis flow by un-supervised technique was showed in Figure 2 In first step of analysis the quality check of the data was

performed in Histogram Namely we cofldidentified the relative

radiancein each wavelength

In second step several training fields showed in Figure 3

were settled in Picture Print which shows the distribution of

resolution by Lines and Columns Line means scan lines and Column

means samples within a scan line of the data In this case the

Line interval and Column interval were reduced for data compression each two intervals Relative radiance scale of 0 to 255 were

displayed in Picture Print

I

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

CHI 1) CHI I Cht 31 113 51 12311 125457142 8312 9668 L847 1615 6019 4421 4846 6919

CHC 4)5644 4012 3268 3073

5 6 7

2075 868

2014

1725 2556 1618

1478 2713 1353

5082 4979 4203

710 348

2189 8 9 10 it 12

953 351 204 365

3471

1478 2955 2292 1502 1490

1187 3226 2128 1149 890

3265 3516 2387 1441 418

1602 1281

842 525 015

CLUSTER VARIANCES

CMI 1) CHI 21 CHI4p CIII 411 17029 4 21 1 3603 2 7760 11278 10088 16013 628 824 1723 758 4 5158 8563 5902 1662 5 289 524 64TS 315 6 1192 2C95 2392 813 7 305 547 703 4178 326 407 1229 5279 1479 2657 3207 2115 10 1411 2039 2494 1005 11 1017 670 1311 636 12 423 139 155 015

Figure 4 Clustering information

5

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2 1 4 5 6 1CLUSTER 1

116 100mints 78 68 650 304 Tar 262 532 176

CLUSTER 11 12

SYMBOL 0 IS

Mils 14L 2L7

Figure 5 2 Field informations(continued)

7

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Figure 6-2 Htstgrams for CLUSTER(continued)

9

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Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

_q

Page 3: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

3973 uimejii

C H U G 0 K U Kob~e

ea of O - kay a a Earima Os k

1973

Kob

- 1 VKO U Himeji-

1 a moa Sea I Osa a

1979

Figure 1 Map ofeatr fof Set Inan Sea Shde

area means the boundary of red tide were sighted

through the year

2

ORIGINAL PACE IS Ok QUALITY

Figure 2 MSS-4 imagery of LAXDSAT-2 acquired over Seto Inland Sea December 30 1975

3

3 Monitoring of sedimentation

Along the southern coast of Hokkaido between Tomakomai and

Urakawa typical expanding pattern of sediment was detected in

MSS-4 imagery As indicated in authors previous report()

MSS-4 imagery is very effective to detect the dritribution of

sediment especially suspended sediment from the river

In Figure 3 expanding pattern from the mouth of Saru River

extended to southwest direction more than 15 Km long Saru River

is noted as polluted water with suspended sediment So the

density of suspended sediment is more concentrative compared with

surrounding area

According to the report issued by Hokkaido Prefecture Saru

River was named depend on its characteristic Namely the river

water contains so much suspended sediment on normal condition

the river was named as River which flows sand in Japanese

S River

iver effluent

Figure 3 MSS-4 Imagery detected the distribution

of sediment June 11 1975

Except the expanding pattern from the mouth of Saru River

the distribution of sediment along the coast was directed to

eastward caused by shore current in this area So stand on the

distribution pattern of sediment shore Current-would be

recognized easily in LANDSAT MSS data obtained at lowest condition

of sea level

4 REpPnUfv Y OF TIM oampGE B P0A

Along the northeast coast of Hokkaidobetween Monbetsu and Abashiri the distribution pattern of sediment was recognized as

the index of shore current in this areaas shown in Figure 4

At the outside of Lake Saroma a round-type pattern indicated by black arrow was detected and it was estimated as sediment

bulges out to the sea through sandy shoals which consist the

outside bank of Lake Saroma

Sediment bul

Monbetsu

Lake Saroma

Figure 4 Sediment bulge detected in MSS-4 imagery June 11 1975

Reference

(1) Hiroaki OchiaiMultidisciplinary Application of LANDSAT-2

Data to Marine Environment in Central Japan Progress

report of LANDSAT-2 investigation

5

288020 RECEIVED BY

NASA STI FACILITY Mdei awallat under NASA swsOCmt DATE- S2 Inthe interest of early ant wide 41s D A NO seminition of Earth Resources SunY DOAF NO

PROCE DProgram intormation and wtthout liability NASA STI FACILITYfor any L-e Made th lreot

QESA-SS ] AI

Title of Investigation 28990 Investigation of Environmental Change Pattern in Japan C76 - 1 O 4 6-

Principal Investigator Dr Takakazu Maruyasu 761 Science University of Tokyo

Noda City Chiba-Ken 278 Japan

Co-Investigator Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mlie-Ken Japan

Date of Submission jiine 30 1976

Quarterly Progress Report for Period

April -June 1976

0rial 0hatography my be cwctund frets EROS Data Center 10th and Dakota Avenue Sioux Falls SD 571 A

REFRDU BYl NATIONAL TECHNICAL INFORMAiON SVICE

SDEPARTMKT OFC SPRINWFIILDVA226

K

Digital Analysis of LANDSAT-2 MSS Dntn STS Codpe 902o in Costal Zone in Central Japan Investigation 28990

No Hiroaki Ochiai July 28 1976

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Introduction

For the purpose of attempt to classify the field informations

revealed by LANDSAT-2 the author tried digital analysis of multishy

spectral scanner data using LARSYS package Although LARSYS was

well known as developed for agriculture remote sensing in first

step depend on the ajustment of the software recently it was

clarified as very effective in various part of remote sensing As

shown in Figure 1 the coastal area in Kii Peninsula Was not

well classified by Photo-interpretation

2 MIS Tape

Before the digital analysis the CCT Tape(9 trucks 1600 BPI) delivered from NASA was reformatted to MIS(Multispectral Image

Storage) Tape which include several Runs data-(l) identification

record(2) data records(3) end-of-file record

3 Analysis flow and result

Analysis flow by un-supervised technique was showed in Figure 2 In first step of analysis the quality check of the data was

performed in Histogram Namely we cofldidentified the relative

radiancein each wavelength

In second step several training fields showed in Figure 3

were settled in Picture Print which shows the distribution of

resolution by Lines and Columns Line means scan lines and Column

means samples within a scan line of the data In this case the

Line interval and Column interval were reduced for data compression each two intervals Relative radiance scale of 0 to 255 were

displayed in Picture Print

I

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

CHI 1) CHI I Cht 31 113 51 12311 125457142 8312 9668 L847 1615 6019 4421 4846 6919

CHC 4)5644 4012 3268 3073

5 6 7

2075 868

2014

1725 2556 1618

1478 2713 1353

5082 4979 4203

710 348

2189 8 9 10 it 12

953 351 204 365

3471

1478 2955 2292 1502 1490

1187 3226 2128 1149 890

3265 3516 2387 1441 418

1602 1281

842 525 015

CLUSTER VARIANCES

CMI 1) CHI 21 CHI4p CIII 411 17029 4 21 1 3603 2 7760 11278 10088 16013 628 824 1723 758 4 5158 8563 5902 1662 5 289 524 64TS 315 6 1192 2C95 2392 813 7 305 547 703 4178 326 407 1229 5279 1479 2657 3207 2115 10 1411 2039 2494 1005 11 1017 670 1311 636 12 423 139 155 015

Figure 4 Clustering information

5

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2 1 4 5 6 1CLUSTER 1

116 100mints 78 68 650 304 Tar 262 532 176

CLUSTER 11 12

SYMBOL 0 IS

Mils 14L 2L7

Figure 5 2 Field informations(continued)

7

LfGATOUy FO APL1C4TIONS OF fNdTt SRIISING J 191197 ptUvOt UIVMKSITy m

LASSYS VIEfStOIN 3

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Figure 6-2 Htstgrams for CLUSTER(continued)

9

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2F I

2 - - 4

12 2 3

52 8060

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112 ICA jl A 1

64 I

321 1 50 01 i

Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

_q

Page 4: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

ORIGINAL PACE IS Ok QUALITY

Figure 2 MSS-4 imagery of LAXDSAT-2 acquired over Seto Inland Sea December 30 1975

3

3 Monitoring of sedimentation

Along the southern coast of Hokkaido between Tomakomai and

Urakawa typical expanding pattern of sediment was detected in

MSS-4 imagery As indicated in authors previous report()

MSS-4 imagery is very effective to detect the dritribution of

sediment especially suspended sediment from the river

In Figure 3 expanding pattern from the mouth of Saru River

extended to southwest direction more than 15 Km long Saru River

is noted as polluted water with suspended sediment So the

density of suspended sediment is more concentrative compared with

surrounding area

According to the report issued by Hokkaido Prefecture Saru

River was named depend on its characteristic Namely the river

water contains so much suspended sediment on normal condition

the river was named as River which flows sand in Japanese

S River

iver effluent

Figure 3 MSS-4 Imagery detected the distribution

of sediment June 11 1975

Except the expanding pattern from the mouth of Saru River

the distribution of sediment along the coast was directed to

eastward caused by shore current in this area So stand on the

distribution pattern of sediment shore Current-would be

recognized easily in LANDSAT MSS data obtained at lowest condition

of sea level

4 REpPnUfv Y OF TIM oampGE B P0A

Along the northeast coast of Hokkaidobetween Monbetsu and Abashiri the distribution pattern of sediment was recognized as

the index of shore current in this areaas shown in Figure 4

At the outside of Lake Saroma a round-type pattern indicated by black arrow was detected and it was estimated as sediment

bulges out to the sea through sandy shoals which consist the

outside bank of Lake Saroma

Sediment bul

Monbetsu

Lake Saroma

Figure 4 Sediment bulge detected in MSS-4 imagery June 11 1975

Reference

(1) Hiroaki OchiaiMultidisciplinary Application of LANDSAT-2

Data to Marine Environment in Central Japan Progress

report of LANDSAT-2 investigation

5

288020 RECEIVED BY

NASA STI FACILITY Mdei awallat under NASA swsOCmt DATE- S2 Inthe interest of early ant wide 41s D A NO seminition of Earth Resources SunY DOAF NO

PROCE DProgram intormation and wtthout liability NASA STI FACILITYfor any L-e Made th lreot

QESA-SS ] AI

Title of Investigation 28990 Investigation of Environmental Change Pattern in Japan C76 - 1 O 4 6-

Principal Investigator Dr Takakazu Maruyasu 761 Science University of Tokyo

Noda City Chiba-Ken 278 Japan

Co-Investigator Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mlie-Ken Japan

Date of Submission jiine 30 1976

Quarterly Progress Report for Period

April -June 1976

0rial 0hatography my be cwctund frets EROS Data Center 10th and Dakota Avenue Sioux Falls SD 571 A

REFRDU BYl NATIONAL TECHNICAL INFORMAiON SVICE

SDEPARTMKT OFC SPRINWFIILDVA226

K

Digital Analysis of LANDSAT-2 MSS Dntn STS Codpe 902o in Costal Zone in Central Japan Investigation 28990

No Hiroaki Ochiai July 28 1976

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Introduction

For the purpose of attempt to classify the field informations

revealed by LANDSAT-2 the author tried digital analysis of multishy

spectral scanner data using LARSYS package Although LARSYS was

well known as developed for agriculture remote sensing in first

step depend on the ajustment of the software recently it was

clarified as very effective in various part of remote sensing As

shown in Figure 1 the coastal area in Kii Peninsula Was not

well classified by Photo-interpretation

2 MIS Tape

Before the digital analysis the CCT Tape(9 trucks 1600 BPI) delivered from NASA was reformatted to MIS(Multispectral Image

Storage) Tape which include several Runs data-(l) identification

record(2) data records(3) end-of-file record

3 Analysis flow and result

Analysis flow by un-supervised technique was showed in Figure 2 In first step of analysis the quality check of the data was

performed in Histogram Namely we cofldidentified the relative

radiancein each wavelength

In second step several training fields showed in Figure 3

were settled in Picture Print which shows the distribution of

resolution by Lines and Columns Line means scan lines and Column

means samples within a scan line of the data In this case the

Line interval and Column interval were reduced for data compression each two intervals Relative radiance scale of 0 to 255 were

displayed in Picture Print

I

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

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I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

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Figure 4 Clustering information

5

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Figure 5 2 Field informations(continued)

7

LfGATOUy FO APL1C4TIONS OF fNdTt SRIISING J 191197 ptUvOt UIVMKSITy m

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4 I

15 4

42

100 305720 00ampa 1044 1290

Figure 6-2 Htstgrams for CLUSTER(continued)

9

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1000 3360 720 0n 104 4 1200

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I 8 I

42 83 36 48

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2F I

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12 2 3

52 8060

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EAC ~ PEPAISNkTS a 0110

112 ICA jl A 1

64 I

321 1 50 01 i

Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

_q

Page 5: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

3 Monitoring of sedimentation

Along the southern coast of Hokkaido between Tomakomai and

Urakawa typical expanding pattern of sediment was detected in

MSS-4 imagery As indicated in authors previous report()

MSS-4 imagery is very effective to detect the dritribution of

sediment especially suspended sediment from the river

In Figure 3 expanding pattern from the mouth of Saru River

extended to southwest direction more than 15 Km long Saru River

is noted as polluted water with suspended sediment So the

density of suspended sediment is more concentrative compared with

surrounding area

According to the report issued by Hokkaido Prefecture Saru

River was named depend on its characteristic Namely the river

water contains so much suspended sediment on normal condition

the river was named as River which flows sand in Japanese

S River

iver effluent

Figure 3 MSS-4 Imagery detected the distribution

of sediment June 11 1975

Except the expanding pattern from the mouth of Saru River

the distribution of sediment along the coast was directed to

eastward caused by shore current in this area So stand on the

distribution pattern of sediment shore Current-would be

recognized easily in LANDSAT MSS data obtained at lowest condition

of sea level

4 REpPnUfv Y OF TIM oampGE B P0A

Along the northeast coast of Hokkaidobetween Monbetsu and Abashiri the distribution pattern of sediment was recognized as

the index of shore current in this areaas shown in Figure 4

At the outside of Lake Saroma a round-type pattern indicated by black arrow was detected and it was estimated as sediment

bulges out to the sea through sandy shoals which consist the

outside bank of Lake Saroma

Sediment bul

Monbetsu

Lake Saroma

Figure 4 Sediment bulge detected in MSS-4 imagery June 11 1975

Reference

(1) Hiroaki OchiaiMultidisciplinary Application of LANDSAT-2

Data to Marine Environment in Central Japan Progress

report of LANDSAT-2 investigation

5

288020 RECEIVED BY

NASA STI FACILITY Mdei awallat under NASA swsOCmt DATE- S2 Inthe interest of early ant wide 41s D A NO seminition of Earth Resources SunY DOAF NO

PROCE DProgram intormation and wtthout liability NASA STI FACILITYfor any L-e Made th lreot

QESA-SS ] AI

Title of Investigation 28990 Investigation of Environmental Change Pattern in Japan C76 - 1 O 4 6-

Principal Investigator Dr Takakazu Maruyasu 761 Science University of Tokyo

Noda City Chiba-Ken 278 Japan

Co-Investigator Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mlie-Ken Japan

Date of Submission jiine 30 1976

Quarterly Progress Report for Period

April -June 1976

0rial 0hatography my be cwctund frets EROS Data Center 10th and Dakota Avenue Sioux Falls SD 571 A

REFRDU BYl NATIONAL TECHNICAL INFORMAiON SVICE

SDEPARTMKT OFC SPRINWFIILDVA226

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Digital Analysis of LANDSAT-2 MSS Dntn STS Codpe 902o in Costal Zone in Central Japan Investigation 28990

No Hiroaki Ochiai July 28 1976

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Introduction

For the purpose of attempt to classify the field informations

revealed by LANDSAT-2 the author tried digital analysis of multishy

spectral scanner data using LARSYS package Although LARSYS was

well known as developed for agriculture remote sensing in first

step depend on the ajustment of the software recently it was

clarified as very effective in various part of remote sensing As

shown in Figure 1 the coastal area in Kii Peninsula Was not

well classified by Photo-interpretation

2 MIS Tape

Before the digital analysis the CCT Tape(9 trucks 1600 BPI) delivered from NASA was reformatted to MIS(Multispectral Image

Storage) Tape which include several Runs data-(l) identification

record(2) data records(3) end-of-file record

3 Analysis flow and result

Analysis flow by un-supervised technique was showed in Figure 2 In first step of analysis the quality check of the data was

performed in Histogram Namely we cofldidentified the relative

radiancein each wavelength

In second step several training fields showed in Figure 3

were settled in Picture Print which shows the distribution of

resolution by Lines and Columns Line means scan lines and Column

means samples within a scan line of the data In this case the

Line interval and Column interval were reduced for data compression each two intervals Relative radiance scale of 0 to 255 were

displayed in Picture Print

I

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

CHI 1) CHI I Cht 31 113 51 12311 125457142 8312 9668 L847 1615 6019 4421 4846 6919

CHC 4)5644 4012 3268 3073

5 6 7

2075 868

2014

1725 2556 1618

1478 2713 1353

5082 4979 4203

710 348

2189 8 9 10 it 12

953 351 204 365

3471

1478 2955 2292 1502 1490

1187 3226 2128 1149 890

3265 3516 2387 1441 418

1602 1281

842 525 015

CLUSTER VARIANCES

CMI 1) CHI 21 CHI4p CIII 411 17029 4 21 1 3603 2 7760 11278 10088 16013 628 824 1723 758 4 5158 8563 5902 1662 5 289 524 64TS 315 6 1192 2C95 2392 813 7 305 547 703 4178 326 407 1229 5279 1479 2657 3207 2115 10 1411 2039 2494 1005 11 1017 670 1311 636 12 423 139 155 015

Figure 4 Clustering information

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116 100mints 78 68 650 304 Tar 262 532 176

CLUSTER 11 12

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Figure 5 2 Field informations(continued)

7

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Figure 6-2 Htstgrams for CLUSTER(continued)

9

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Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

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4 4 230

6 868 9 351

6 7 2014 8 953

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11 365

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Figure a Grouping of CLUSTER

12

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

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Page 6: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

Along the northeast coast of Hokkaidobetween Monbetsu and Abashiri the distribution pattern of sediment was recognized as

the index of shore current in this areaas shown in Figure 4

At the outside of Lake Saroma a round-type pattern indicated by black arrow was detected and it was estimated as sediment

bulges out to the sea through sandy shoals which consist the

outside bank of Lake Saroma

Sediment bul

Monbetsu

Lake Saroma

Figure 4 Sediment bulge detected in MSS-4 imagery June 11 1975

Reference

(1) Hiroaki OchiaiMultidisciplinary Application of LANDSAT-2

Data to Marine Environment in Central Japan Progress

report of LANDSAT-2 investigation

5

288020 RECEIVED BY

NASA STI FACILITY Mdei awallat under NASA swsOCmt DATE- S2 Inthe interest of early ant wide 41s D A NO seminition of Earth Resources SunY DOAF NO

PROCE DProgram intormation and wtthout liability NASA STI FACILITYfor any L-e Made th lreot

QESA-SS ] AI

Title of Investigation 28990 Investigation of Environmental Change Pattern in Japan C76 - 1 O 4 6-

Principal Investigator Dr Takakazu Maruyasu 761 Science University of Tokyo

Noda City Chiba-Ken 278 Japan

Co-Investigator Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mlie-Ken Japan

Date of Submission jiine 30 1976

Quarterly Progress Report for Period

April -June 1976

0rial 0hatography my be cwctund frets EROS Data Center 10th and Dakota Avenue Sioux Falls SD 571 A

REFRDU BYl NATIONAL TECHNICAL INFORMAiON SVICE

SDEPARTMKT OFC SPRINWFIILDVA226

K

Digital Analysis of LANDSAT-2 MSS Dntn STS Codpe 902o in Costal Zone in Central Japan Investigation 28990

No Hiroaki Ochiai July 28 1976

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Introduction

For the purpose of attempt to classify the field informations

revealed by LANDSAT-2 the author tried digital analysis of multishy

spectral scanner data using LARSYS package Although LARSYS was

well known as developed for agriculture remote sensing in first

step depend on the ajustment of the software recently it was

clarified as very effective in various part of remote sensing As

shown in Figure 1 the coastal area in Kii Peninsula Was not

well classified by Photo-interpretation

2 MIS Tape

Before the digital analysis the CCT Tape(9 trucks 1600 BPI) delivered from NASA was reformatted to MIS(Multispectral Image

Storage) Tape which include several Runs data-(l) identification

record(2) data records(3) end-of-file record

3 Analysis flow and result

Analysis flow by un-supervised technique was showed in Figure 2 In first step of analysis the quality check of the data was

performed in Histogram Namely we cofldidentified the relative

radiancein each wavelength

In second step several training fields showed in Figure 3

were settled in Picture Print which shows the distribution of

resolution by Lines and Columns Line means scan lines and Column

means samples within a scan line of the data In this case the

Line interval and Column interval were reduced for data compression each two intervals Relative radiance scale of 0 to 255 were

displayed in Picture Print

I

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

CHI 1) CHI I Cht 31 113 51 12311 125457142 8312 9668 L847 1615 6019 4421 4846 6919

CHC 4)5644 4012 3268 3073

5 6 7

2075 868

2014

1725 2556 1618

1478 2713 1353

5082 4979 4203

710 348

2189 8 9 10 it 12

953 351 204 365

3471

1478 2955 2292 1502 1490

1187 3226 2128 1149 890

3265 3516 2387 1441 418

1602 1281

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CLUSTER VARIANCES

CMI 1) CHI 21 CHI4p CIII 411 17029 4 21 1 3603 2 7760 11278 10088 16013 628 824 1723 758 4 5158 8563 5902 1662 5 289 524 64TS 315 6 1192 2C95 2392 813 7 305 547 703 4178 326 407 1229 5279 1479 2657 3207 2115 10 1411 2039 2494 1005 11 1017 670 1311 636 12 423 139 155 015

Figure 4 Clustering information

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ELUSIER 1 3 4 5 6 7 0 9 10

synot A 7 2 V T a x N

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19VPOINTS 31 4samp -0020

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ass ON

UveEamp OF POINTS PFA CLUSTER

2 1 4 5 6 1CLUSTER 1

116 100mints 78 68 650 304 Tar 262 532 176

CLUSTER 11 12

SYMBOL 0 IS

Mils 14L 2L7

Figure 5 2 Field informations(continued)

7

LfGATOUy FO APL1C4TIONS OF fNdTt SRIISING J 191197 ptUvOt UIVMKSITy m

LASSYS VIEfStOIN 3

IT gRAS FOR CLjSTEF CLASS 4 ttL HUMSa OF SAPLES 230

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2a 26 I

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1 I 61 fl2 441

1 I 5 lj 44 1

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5720 90AO 1044 120

4 I

15 4

42

100 305720 00ampa 1044 1290

Figure 6-2 Htstgrams for CLUSTER(continued)

9

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LARSYS VENS10PN3

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ilSTOamptA4451

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42 1

In 2413)l= 24tS31Z Us 1

1000 3360 720 0n 104 4 1200

PI 2

EACH - RtPROSINTS pailil)

CHA -EL 60- 010 flCRtItfS

I 8 I

42 83 36 48

2 1 255 I 22

370 C 90 J1CCCOETEt(S

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3t T I 4e844 l

00305720 808 pound04iO4 1200

2F I

2 - - 4

12 2 3

52 8060

Dur-i 130 r80 (RcniuEdRS

EAC ~ PEPAISNkTS a 0110

112 ICA jl A 1

64 I

321 1 50 01 i

Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Kii Peninsula near Shionomisaki

AI

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Page 7: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

288020 RECEIVED BY

NASA STI FACILITY Mdei awallat under NASA swsOCmt DATE- S2 Inthe interest of early ant wide 41s D A NO seminition of Earth Resources SunY DOAF NO

PROCE DProgram intormation and wtthout liability NASA STI FACILITYfor any L-e Made th lreot

QESA-SS ] AI

Title of Investigation 28990 Investigation of Environmental Change Pattern in Japan C76 - 1 O 4 6-

Principal Investigator Dr Takakazu Maruyasu 761 Science University of Tokyo

Noda City Chiba-Ken 278 Japan

Co-Investigator Hiroaki Ochiai

Toba Merchant Marine College

Toba City Mlie-Ken Japan

Date of Submission jiine 30 1976

Quarterly Progress Report for Period

April -June 1976

0rial 0hatography my be cwctund frets EROS Data Center 10th and Dakota Avenue Sioux Falls SD 571 A

REFRDU BYl NATIONAL TECHNICAL INFORMAiON SVICE

SDEPARTMKT OFC SPRINWFIILDVA226

K

Digital Analysis of LANDSAT-2 MSS Dntn STS Codpe 902o in Costal Zone in Central Japan Investigation 28990

No Hiroaki Ochiai July 28 1976

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Introduction

For the purpose of attempt to classify the field informations

revealed by LANDSAT-2 the author tried digital analysis of multishy

spectral scanner data using LARSYS package Although LARSYS was

well known as developed for agriculture remote sensing in first

step depend on the ajustment of the software recently it was

clarified as very effective in various part of remote sensing As

shown in Figure 1 the coastal area in Kii Peninsula Was not

well classified by Photo-interpretation

2 MIS Tape

Before the digital analysis the CCT Tape(9 trucks 1600 BPI) delivered from NASA was reformatted to MIS(Multispectral Image

Storage) Tape which include several Runs data-(l) identification

record(2) data records(3) end-of-file record

3 Analysis flow and result

Analysis flow by un-supervised technique was showed in Figure 2 In first step of analysis the quality check of the data was

performed in Histogram Namely we cofldidentified the relative

radiancein each wavelength

In second step several training fields showed in Figure 3

were settled in Picture Print which shows the distribution of

resolution by Lines and Columns Line means scan lines and Column

means samples within a scan line of the data In this case the

Line interval and Column interval were reduced for data compression each two intervals Relative radiance scale of 0 to 255 were

displayed in Picture Print

I

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

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I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

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Figure 4 Clustering information

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7

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10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

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1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

_q

Page 8: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

Digital Analysis of LANDSAT-2 MSS Dntn STS Codpe 902o in Costal Zone in Central Japan Investigation 28990

No Hiroaki Ochiai July 28 1976

Toba Merchant Marine College

Toba City Mie-Ken Japan

1 Introduction

For the purpose of attempt to classify the field informations

revealed by LANDSAT-2 the author tried digital analysis of multishy

spectral scanner data using LARSYS package Although LARSYS was

well known as developed for agriculture remote sensing in first

step depend on the ajustment of the software recently it was

clarified as very effective in various part of remote sensing As

shown in Figure 1 the coastal area in Kii Peninsula Was not

well classified by Photo-interpretation

2 MIS Tape

Before the digital analysis the CCT Tape(9 trucks 1600 BPI) delivered from NASA was reformatted to MIS(Multispectral Image

Storage) Tape which include several Runs data-(l) identification

record(2) data records(3) end-of-file record

3 Analysis flow and result

Analysis flow by un-supervised technique was showed in Figure 2 In first step of analysis the quality check of the data was

performed in Histogram Namely we cofldidentified the relative

radiancein each wavelength

In second step several training fields showed in Figure 3

were settled in Picture Print which shows the distribution of

resolution by Lines and Columns Line means scan lines and Column

means samples within a scan line of the data In this case the

Line interval and Column interval were reduced for data compression each two intervals Relative radiance scale of 0 to 255 were

displayed in Picture Print

I

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

CHI 1) CHI I Cht 31 113 51 12311 125457142 8312 9668 L847 1615 6019 4421 4846 6919

CHC 4)5644 4012 3268 3073

5 6 7

2075 868

2014

1725 2556 1618

1478 2713 1353

5082 4979 4203

710 348

2189 8 9 10 it 12

953 351 204 365

3471

1478 2955 2292 1502 1490

1187 3226 2128 1149 890

3265 3516 2387 1441 418

1602 1281

842 525 015

CLUSTER VARIANCES

CMI 1) CHI 21 CHI4p CIII 411 17029 4 21 1 3603 2 7760 11278 10088 16013 628 824 1723 758 4 5158 8563 5902 1662 5 289 524 64TS 315 6 1192 2C95 2392 813 7 305 547 703 4178 326 407 1229 5279 1479 2657 3207 2115 10 1411 2039 2494 1005 11 1017 670 1311 636 12 423 139 155 015

Figure 4 Clustering information

5

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UveEamp OF POINTS PFA CLUSTER

2 1 4 5 6 1CLUSTER 1

116 100mints 78 68 650 304 Tar 262 532 176

CLUSTER 11 12

SYMBOL 0 IS

Mils 14L 2L7

Figure 5 2 Field informations(continued)

7

LfGATOUy FO APL1C4TIONS OF fNdTt SRIISING J 191197 ptUvOt UIVMKSITy m

LASSYS VIEfStOIN 3

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Figure 6-2 Htstgrams for CLUSTER(continued)

9

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2 1 255 I 22

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2F I

2 - - 4

12 2 3

52 8060

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EAC ~ PEPAISNkTS a 0110

112 ICA jl A 1

64 I

321 1 50 01 i

Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

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Page 9: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

In third step Clustered information were calculated on three

points and Cluster yrocessor Information were as follows-

ID Number I

Maximum Classes 12

Convergence 999

Minimum Field Size 4

Interval I

As shown in Figure 4 Cluster Points Means and Cluster

Variances were carried out statastically For example for Class

4 Cluster Points Means indicated as 4421 to channel 1 4846 to

channel 2 6919 to channel 3 and 3073 to channel 4 Compared

with Histograms for Cluster showed in Figure 6-1 Cluster Points

Means were identified satisfactorily Cluster Variances were also

well identified to each wavelength For Class 4 5158 to channel

1 8563 to channel 2 5902 to channel 3 and 1662 to channel 4

In training field showed in upper part of Figure 5-1 Number

of Points per Cluster were classified by Symbol and total Points

for each Class were calculated General total of Samples were

identified as 2074

In fourth step Separability Information were calculated in

Cluster as shown in Figure 7 Separability between Classes of

interest as a function of combinations of spectral bandswere

also very important for satistical analysis of multiapectral

scanner data The best evaluation of quotient in LARSYS was known

as 075

In fifth step Cluster Grouping were defined to nine classes

as shown in Figure 8 In this case Cluster 3 and 5 were grouped

to 3 Cluster 6 and 9 were grouped to 5 and Cluster 7 and 8 were

grouped to 6

In sixth step Classification by per field analysis were

carried out as shown in Figure 9 In these Classification Maps

we could obtained qualtative determination of the classification

Reference

(I4 LARS Annual Report-Vol 4(1970) p 7 - 40

2

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

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I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

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5 6 7

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2014

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3471

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1602 1281

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Figure 4 Clustering information

5

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Figure 5 2 Field informations(continued)

7

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CHNNEL 420t0 -110 NICRoMITEtS EACH4lPftSEf IS

21t is

5720 90AO 1044 120

4 I

15 4

42

100 305720 00ampa 1044 1290

Figure 6-2 Htstgrams for CLUSTER(continued)

9

LASCLATtUY FOP APPL1CTIUAS F EP TE SfSNtSI JUNE IS157t PUROW U IVEkSIY 2 31 Ol p4

LARSYS VENS10PN3

-ISTOkAn S FOr CLUSTiamp CLASS 9 TOTAl kfitVl OF SPpLESVLS

ilSTOamptA4451

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42 1

In 2413)l= 24tS31Z Us 1

1000 3360 720 0n 104 4 1200

PI 2

EACH - RtPROSINTS pailil)

CHA -EL 60- 010 flCRtItfS

I 8 I

42 83 36 48

2 1 255 I 22

370 C 90 J1CCCOETEt(S

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3t T I 4e844 l

00305720 808 pound04iO4 1200

2F I

2 - - 4

12 2 3

52 8060

Dur-i 130 r80 (RcniuEdRS

EAC ~ PEPAISNkTS a 0110

112 ICA jl A 1

64 I

321 1 50 01 i

Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 9-1 Classification map of nort iern part of Kii Peninsula near Kumano City

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

_q

Page 10: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

Kumano r Ybull city

Shionomisaki

MSS-5 imagery 4SS-7 imagery

Figure I Enlarged LANDSAT-2 imageries Sept 11 1976

3

Zape

Mltiapectral

Im ge Storage

HI S

PIC

CLI

Stistics Fie

Classified iFields

Test Fields

nSClassificatio

Rsults

LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

CHI 1) CHI I Cht 31 113 51 12311 125457142 8312 9668 L847 1615 6019 4421 4846 6919

CHC 4)5644 4012 3268 3073

5 6 7

2075 868

2014

1725 2556 1618

1478 2713 1353

5082 4979 4203

710 348

2189 8 9 10 it 12

953 351 204 365

3471

1478 2955 2292 1502 1490

1187 3226 2128 1149 890

3265 3516 2387 1441 418

1602 1281

842 525 015

CLUSTER VARIANCES

CMI 1) CHI 21 CHI4p CIII 411 17029 4 21 1 3603 2 7760 11278 10088 16013 628 824 1723 758 4 5158 8563 5902 1662 5 289 524 64TS 315 6 1192 2C95 2392 813 7 305 547 703 4178 326 407 1229 5279 1479 2657 3207 2115 10 1411 2039 2494 1005 11 1017 670 1311 636 12 423 139 155 015

Figure 4 Clustering information

5

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2 1 4 5 6 1CLUSTER 1

116 100mints 78 68 650 304 Tar 262 532 176

CLUSTER 11 12

SYMBOL 0 IS

Mils 14L 2L7

Figure 5 2 Field informations(continued)

7

LfGATOUy FO APL1C4TIONS OF fNdTt SRIISING J 191197 ptUvOt UIVMKSITy m

LASSYS VIEfStOIN 3

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Figure 6-2 Htstgrams for CLUSTER(continued)

9

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2 1 255 I 22

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2F I

2 - - 4

12 2 3

52 8060

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112 ICA jl A 1

64 I

321 1 50 01 i

Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

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Zape

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LARSYS analysis flow(Un-SperisedFigure 2 classification)

4

FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

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Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

CHI 1) CHI I Cht 31 113 51 12311 125457142 8312 9668 L847 1615 6019 4421 4846 6919

CHC 4)5644 4012 3268 3073

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1725 2556 1618

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3471

1478 2955 2292 1502 1490

1187 3226 2128 1149 890

3265 3516 2387 1441 418

1602 1281

842 525 015

CLUSTER VARIANCES

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Figure 4 Clustering information

5

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Figure 5 2 Field informations(continued)

7

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9

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Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

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1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

---

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Figure 9-1 Classification map of nort iern part of Kii Peninsula near Kumano City

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

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FIELCS TO eE CLUSTERED LISTED IN ORDER PACCESSED

RU FIELU FIRST LAST LINE FIkST LAST COLUMNNUMbER DESIG LINE LINE INI COLUMN COLUMN INT

I 75C911CO 1 100 3 700 820 2 2 75CS11CO 27C 400 3 500 580 2 3 75091100 560 620 3 300 590 2

4 75CIICO 700 850 3 200 400 2

Figure 3 Training field

CLUSTERING INFORMATION

NUMBER OF CLUSTERS = 12 CLUSTERING UNIT SIZE = 12095 CLUSTERING INTERVAL =I CHANNEL NJMBER 1 SPECTRAL RANGE 050 TO 060 MICROMETERS CALIBRATION

CHANNEL NUMBER 2 SPECTRAL RANGE 060 TO 070 MICROMETERS CALIBRATION CHANNEL NUMBER 3 SPECTRAL RANGE 070 TO 080 MICROMETERS CALIBRATION

CHANNEL MJtMBER 4 SPECTRAL RANGE 080 TO 110 MICROMETERS CALIBRATION

CLUSTER POINTS MEANS

I 2 3 4

111 137

1316 230

CHI 1) CHI I Cht 31 113 51 12311 125457142 8312 9668 L847 1615 6019 4421 4846 6919

CHC 4)5644 4012 3268 3073

5 6 7

2075 868

2014

1725 2556 1618

1478 2713 1353

5082 4979 4203

710 348

2189 8 9 10 it 12

953 351 204 365

3471

1478 2955 2292 1502 1490

1187 3226 2128 1149 890

3265 3516 2387 1441 418

1602 1281

842 525 015

CLUSTER VARIANCES

CMI 1) CHI 21 CHI4p CIII 411 17029 4 21 1 3603 2 7760 11278 10088 16013 628 824 1723 758 4 5158 8563 5902 1662 5 289 524 64TS 315 6 1192 2C95 2392 813 7 305 547 703 4178 326 407 1229 5279 1479 2657 3207 2115 10 1411 2039 2494 1005 11 1017 670 1311 636 12 423 139 155 015

Figure 4 Clustering information

5

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2 1 4 5 6 1CLUSTER 1

116 100mints 78 68 650 304 Tar 262 532 176

CLUSTER 11 12

SYMBOL 0 IS

Mils 14L 2L7

Figure 5 2 Field informations(continued)

7

LfGATOUy FO APL1C4TIONS OF fNdTt SRIISING J 191197 ptUvOt UIVMKSITy m

LASSYS VIEfStOIN 3

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Figure 6-2 Htstgrams for CLUSTER(continued)

9

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2F I

2 - - 4

12 2 3

52 8060

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112 ICA jl A 1

64 I

321 1 50 01 i

Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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Figure 5 2 Field informations(continued)

7

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10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

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11 365

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SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

---

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Figure 9-1 Classification map of nort iern part of Kii Peninsula near Kumano City

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Figure 9-2 Classification map of southern part of

Kii Peninsula near Shionomisaki

AI

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Figure 6-2 Htstgrams for CLUSTER(continued)

9

LASCLATtUY FOP APPL1CTIUAS F EP TE SfSNtSI JUNE IS157t PUROW U IVEkSIY 2 31 Ol p4

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1000 3360 720 0n 104 4 1200

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Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

---

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Figure 9-1 Classification map of nort iern part of Kii Peninsula near Kumano City

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Kii Peninsula near Shionomisaki

AI

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5720 90AO 1044 120

4 I

15 4

42

100 305720 00ampa 1044 1290

Figure 6-2 Htstgrams for CLUSTER(continued)

9

LASCLATtUY FOP APPL1CTIUAS F EP TE SfSNtSI JUNE IS157t PUROW U IVEkSIY 2 31 Ol p4

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1000 3360 720 0n 104 4 1200

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2 1 255 I 22

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12 2 3

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112 ICA jl A 1

64 I

321 1 50 01 i

Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

---

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LASCLATtUY FOP APPL1CTIUAS F EP TE SfSNtSI JUNE IS157t PUROW U IVEkSIY 2 31 Ol p4

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Figure 6- 3 Histgrama for CLUSTMR (continued)

10

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

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Figure a Grouping of CLUSTER

12

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Figure 9-1 Classification map of nort iern part of Kii Peninsula near Kumano City

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Page 18: available under NASA sponsorShip 7.6- 1 0. 46 8. OA€¦ · "VMade available under NASA sponsorShip in the. interest of early and wide dis-7.6-1 . 0. 46 8. semination of Earth Resources

LABCRATCRY FOR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

SEPARABILITY INFORMATION

I J D([JI D() 00) D(I)+D(JI QUOT

1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1O1 I1

66820 159051 119171 165628 154142 17214q179913 159220 176639192634

17S58 16106 15UEC 15737 15226 15204 14606 13442 1322413146

1934 9476 25706 6676

12951 686 7504 13457 112168058

3689L 25582 41566 22413 28184 22067 22110 26898 24440 21204

1811 6217 2867 1390 5469 7801 81 7 59 9 7228 9085

1 12 2 3 2 4 2 5 z 6 2

B9

2 10 2 11 2 12 3 4 3 5 3 6 3 7 3 a 3 93 10 3 11

201606 93144 52778 99386 87831 105896 11386794161 111728 127650 137325 42326 11064 19070 21412 32680 3748244208 53681

12515 32828 38893 37093 38897 39109 38498 31559 33211 34411 33159 9108 L1872 8845 11865 11873 967411081 11734

3049 9578

30485 6922 14405 7018 7603 13724 12141 7747 2916

26963 7271

1191l 7603 10090 1725312 909 9721

15564 42406 69379 44015 53302 46127 46100 45284 45352 42157 36074 36071 L9143 20756 19468 21963 2692723989 21455

12953 2196 0761 2258 1648 2296 2470 2079 2464 3028 3807 1173 0578 0919 L100 1488 13926483 2502

3 12 4 5 4 6 4 7 4 8

65279 47031 35099 53122 61292

1123 29812 28461 30215 29306

1765 6821 14265 6995 7836

13288 36632 42726 37210 37142

4912 1284 0821 1428 1650

4 q4 10 4 11 4 12 5 6 5 7 5 a 5 9 5 1o 5 1 5 12 6 7 6 8 6 9 6 11 6 11 6 12

44216 61188 76603 87099 L5349 10348 21617 30121 33906 42641 54237 18321 26427 19249 30659 4403555414

23384 25163 26213 25148 6599 7270 7259 6621 6908 7221 7163 14344 14315 11719 12666 1386 13482

14012 12C09 8091 2523 12858 7596

10091 17304 13254 9762 1768 69527916 16902 1279 8502 2119

37396 37172 34303 27671 19457 14866 17351 23925 20162 16983 8931

21295 22231 28621 2543 22307 15600

1182 1646 2233 3148 0789 0696 1246 1259 1682 2511 6073 0860 1189 0673 1205 1974 3552

7 8 79 7 10 7 11 7 12 8 9 8 10 a 11 a 12 9 10 9 it 9 12

11274 25 03 24838 32325 43915 2553 17025 21185 32734 17664 33635 43385

7671 7044 7359 553 7351 6029 66C4 9510 9171 L3382 13474 13132

10083 16480 13814

9 8 41770

14771 15090 9871 1791 12681 7076 2840

17754 23523 21173 17377 9122 20800 21694 19387 10962 26C68 20550 15972

0635 1093 1173 1860 4814 1228 0785 1093 2986 0678 1637 2Tb

tO 11 10 12 11 12

16049 2595 11729

12408 12774 8212

7484 2680 1990

19893 15454 10202

C807 1680 1150

AVERAGE QUOTIENT 2655

Figure 7 Separability information

calculated in CLUSTER

11

LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

ThRESHOLD = 075

GROUP CLUSTERS NO PTS

L Lit

2 2 L37

3 3 1316 5 2075

4 4 230

6 868 9 351

6 7 2014 8 953

7 LO 204

11 365

12 347L

Figure a Grouping of CLUSTER

12

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LAdORATOkY FCR APPLICATIONS OF REMOTE SENSING PURDUE UNIVERSITY

RESULTS OF CLUSTER GROUPING

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Figure 9-2 Classification map of southern part of

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Classified 7.6

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ESA 7.6 Configuration Guide

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Chapter 7 Lesson 7.6 Random Variables and Probability Distributions 7.6: Normal Distributions.

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Section 7.6.A

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7.6 Book Answers

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7.6 Percentage Applications

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7.6 EXPONENTIAL FUNCTIONS: