Automatic Generation of Land-Use Maps
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Transcript of Automatic Generation of Land-Use Maps
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 14
Automatic generation of land-use maps for a spatialdecision support system for Puerto Rico
Johannes van der Kwast
Josefien DelrueLuc BertelsInge Uljee
Stijn Van LooyJoan Schepens
and Guy EngelenUnit Environmental Modelling
VITO
Mol Belgium
Email hansvanderkwastvitobe
Elias Gutierrez
Graduate School of PlanningUniversity of Puerto Rico
San Juan Puerto Rico
Email eliasgutierrezyahoocom
Glenda Roman
Geographic Mapping Technologies CorpSan Juan Puerto Rico
Email gromangmtgiscom
Abstractmdash This study proposes an automatic image processingprocedure in order to facilitate regular updating of the land-usemap of Puerto Rico which is a key dataset for the Xplorah Plan-ning Support Systems The procedure is based on the contextualreclassification of digital high resolution aerial photographs thatwere preclassified using a decision tree classifier For the contex-tual reclassification the Optimized Spatial Reclassification Kernel(OSPARK) is used which is able to discriminate functional land-use classes and land cover based on the configuration of objects ina kernel A unique property of OSPARK is that it automaticallyadapts the kernel size as a function of spatial variation in theneighborhood of each pixel to be classified The processing chainhas been implemented on a computer cluster which enablesparallel processing Classification results were evaluated usingindependent land-use data derived from visual interpretation Itcan be concluded that the procedure gives good classificationresults for the tiles that are used to train the algorithm butthat the extrapolation to other tiles resulted in much loweraccuracies Error sources have been identified and suggestionsfor improvements are given
I INTRODUCTION
The Xplorah Planning Support Systems developed for the
Puerto Rico Planning Board enables planners and policy
makers to forecast land-use changes as the result of various
scenarios and to assess alternative planning and policy options
in their fully integrated dynamic and spatial context The
quality of land use predicted by Xplorah as well as other land-use change models relies heavily on the availability of high
quality geographically referenced data A high quality time
series of land-use maps is necessary for calibration validation
and updating of the model Land-use maps however are often
lacking Even if time series are available inconsistencies in
mapping methodologies legends and scales often induce mea-
sured land-use changes that do not represent actual changes
in land-use patterns Furthermore land-use maps are mainly
derived from manual mapping which is time-consuming and
expensive
This study evaluates the feasibility of using an automatic
image processing procedure in order to update the land-use
map to be used in Xplorah The aim is to automatically
derive land-use maps at 60 m resolution from digital aerial
photographs with a classification accuracy of ge66 The
procedure proposed in this study uses a contextual reclassi-
fication algorithm applied to a preliminary classification of
digital aerial photographs The processing chain has been
implemented on a computer cluster which enables parallel
processing
II REMOTE SENSING AND G IS DATA
In the period from October to December 2009 thousands of
multispectral images were acquired over Puerto Rico using
the ADS40 SH52 digital image sensor of Fugro Earthdata Inc
Each frame covers 10K by 10K pixels in four spectral bands
(red green blue and near-infrared) Flying at an altitude of
2900 m a ground resolution of 03 m was obtained Histogram
matching was applied during image pre-processing in order to
ensure that all images have a comparable reflectance
The reference land-use data consists of the Xplorah 2010
land-use map at 60 m resolution which has been developed as
part of the Xplorah project [1] The map is derived by means of visual interpretation using remote sensing data supplemented
with ancillary datasets The reported accuracy of the Xplorah
2010 land-use map is 97 although it should be noted that
this land-use map is a representation of reality with its inherent
uncertainties that are difficult to quantify The goal of the
remote sensing based classification however is to produce
land-use maps similar to the Xplorah land-use map with higher
temporal availability and less costs Therefore it should be
noted that the statistics derived from the comparison between
the automatic classification and the reference map do not
necessarily reflect disagreement with reality
978-1-4244-8657-111$2600 c⃝2011 IEEE
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 24
Fig 1 Flowchart of the OSPARK algorithm The shaded part shows theoriginal SPARK algorithm that is iterated for a range of kernel sizes in theOSPARK algorithm [5]
III THE OSPARK ALGORITHM
The Optimized SPARK (OSPARK) algorithm [2] is a con-
textual reclassifier which is based on the Spatial Reclas-
sification Kernel (SPARK [3]) Contextual reclassifiers are
based on the concept that information captured in neighboring
cells or information about patterns surrounding the pixel of
interest may provide useful supplementary information in the
classification process [4] Previous research [3] has demon-
strated a strong relationship between the spatial structure of
urban areas and its functional characteristics The SPARK
algorithm examines the local spatial patterns of land cover
in a square kernel or moving window and classifies the center
pixel based on the arrangement of adjacent pixels OSPARK
is an extension to SPARK in the sense that it automatically
adapts the kernel size to the spatial variation detected around
the pixel to be classified The classification consists of three
phases [3]
1) Producing a land-cover map using any type of pixel-
based spectral classifier from a remotely sensed image
further referred to as lsquoinitial land-cover maprsquo
2) Defining decision rules based on local spatial patterns
of land cover in typical land-use types
3) Reclassifying the initial land-cover map into land-use
types based on the decision rules of phase 2
Fig 1 shows the flowchart of the OSPARK algorithm The
algorithm derives adjacency event matrices M by counting the
frequency of the pixel-based classes positioned next to each
other as well as diagonally within each template kernel Next
the M-matrices are compared with template (T) matrices thatare derived from kernels that are representative for the land-
use classes to be derived The similarity index is used as
a goodness-of-fit measure
= 1 minus
09830865 minus2
1038389sum1103925=1
1038389sum907317=1
98308010383891103925907317 minus 11039251038389
2(1)
where 10383891103925907317 is the adjacency event in a 907317 by 907317 matrix M 11039251038389 is
the adjacency event in a 907317 by 907317 matrix T which is a template
matrix for land-use class is the total number of adjacency
983154983156983144983151983152983144983151983156983151983155983090983088983089983088
983089983088983147 983160 983089983088983147 983156983145983148983141983155983104 983088983086983091 983149
983113983150983145983156983145983137983148983148983137983150983140‐983139983151983158983141983154 983149983137983152
983089983147 983160 983089983147 983156983145983148983141983155983104 983091 983149
Resampling
Conversion to IDL
983123983120983105983122983115 983148983137983150983140‐983157983155983141983149983137983152
983091983147 983160 983091983147 983156983145983148983141983155983104 983091 983149
Conversion to
PCRaster
983123983120983105983122983115 983148983137983150983140‐983157983155983141983149983137983152
983104 983089983093 983149
983104 983094983088 983149
983104 983090983092983088 983149
983113983150983145983156983145983137983148983148983137983150983140‐983139983151983158983141983154 983149983137983152 983151983142
983156983154983137983145983150983145983150983143 983156983145983148983141983155
983123983120983105983122983115 983148983137983150983140‐983157983155983141983149983137983152 983151983142
983156983154983137983145983150983145983150983143 983156983145983148983141983155
983122983141983142983141983154983141983150983139983141 983148983137983150983140‐983157983155983141983149983137983152
983104 983089983093 983149
983104 983094983088 983149
983104 983090983092983088 983149
Resampling
Reclass
983113983150983145983156983145983137983148983148983137983150983140‐983139983151983158983141983154 983149983137983152
983091983147 983160 983091983147 983156983145983148983141983155983104 983091 983149983124983141983149983152983148983137983156983141
983140983137983156983137983138983137983155983141
983123983120983105983122983115
983137983148983143983151983154983145983156983144983149
Histogram matching
GDAL retile
Mosaick
983126983137983148983145983140983137983156983145983151983150
Sample
templates
Fig 2 Flowchart of the classification procedure
events in the kernel and 907317 is the number of classes in the per-
pixel classified input map can range from 0 to 1 If
equals 0 M is completely different from T while a value
of 1 means that they are identical
OSPARK iteratively calculates the similarity index for ker-
nel sizes with an apothem ie distance from the center pixelto a side of a square kernel from 1 to pixels The resulting
stack consisting of similarity maps is analyzed by an
integration operator which assigns the class that corresponds
with the optimal -value for each pixel The optimal -
value is determined based on two possible cases for the
evolution of with increasing kernel size [2]
1) In the case that local maxima are present the first local
maximum above a user-defined minimum -threshold
value is determined and the corresponding land-use class
is assigned
2) In the case that local maxima are absent the curve
converges to
asymp 1
and the integration operator assignsthe class to the pixel when the -value changes less
than 005 between consecutive iterations and is higher
than the threshold value
The threshold prevents classification of pixels with a too
low -value
The derived land-use map and a map containing the -
value corresponding to the optimal kernel size for each pixel
are the outputs of the algorithm
IV THE PROCESSING CHAIN
Fig 2 shows the workflow for the automatic classification of
the orthorectified aerial photographs of 2009 The procedure
consists of preprocessing building the template database run-ning OSPARK in batch on a computer cluster post-processing
and accuracy assessment
A Preprocessing
First the 1500 orthophoto tiles were in batch converted to
the IDL ENVI image format and resampled to tiles of 1000
by 1000 pixels with 3 m resolution Next each tile containing
blue green red and near-infrared channels was classified
using a decision tree classification The decision tree classifier
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 34
Legend
NATURAL
FOREST
AGRICULTURE
CONSTRUCTION
MINING
INDUSTRIAL
HIGH-DENSITYTRA DE AND SERVICES
HIGH-DENSITY RESIDENTIAL
FORESTRESERVES
MANGROVES AND SWAMPS
SEA
BEACH
CORALREEF
WATER RESOURCES
PUBLIC AND RECREATION
UTILITIES
INFRASTRUCTURE
ROCKYCLIFF S AND SHELVES
RANGELANDS
LOW-DENSITY TRADE AND SERVICES
LOW-DENSITY RESIDENTIAL
0 20 4010Kilometers
1
2
3
Fig 3 Training tiles for building the OSPARK template database 1 =urbanized area (San Juan) 2 = naturalrural area (around Bosque Estatal deMonte Guilarte) 3 = urbanized area (Mayaguez) The background map showsthe OSPARK classification result
is an unsupervised classification method that performs a multi-
stage classification by using a series of binary decisions inorder to cluster pixels This procedure resulted in 25 classes
3 m was considered as the most optimal resolution for the
initial land-cover map as the objects could be clearly defined
at this resolution while noise introduced by unnecessary
spatial detail was avoided The initial land-cover map was
retiled to tiles with 3000 by 3000 pixels and converted to
the PCRaster format which is the input for the OSPARK
algorithm Open-source utilities distributed with the Geospatial
Data Abstraction Library (GDAL httpwwwgdalorg) were
used to perform this The size of the tiles was considered as
optimal since small tiles would result in many missing values
after the OSPARK classification while larger tiles could causethe system to run out of memory Separate tiles were calculated
to cover the tile edges that will have missing values after the
OSPARK classification In total 178 tiles of 3000 by 3000
pixels 150 tiles of 100 by 3000 (row by columns) and 166 tiles
of 3000 by 100 were used to classify the entire Commonwealth
of Puerto Rico
B Building the template database
The OSPARK algorithm needs a database of representative
template matrices For this purpose three 3000 by 3000 tiles
were selected (Fig 3)
These training tiles were selected in order to include the
most important land-use classes involved in urban dynamics
but also to represent natural and rural land-use classes The
center coordinates of the template kernels were derived by
stratified random sampling of 50 points within each class of
the land-use map The same procedure was followed to derive
an independent set of pixels for evaluation of the contextual
classification of the tiles
In order to check the quality of the selected templates and
their transferability to different areas different combinations
of templates have been used in the OSPARK classifications
of the three tiles The resulting maps were evaluated using
contingency matrices with the independent reference data
sampled from the Xplorah 2010 land-use map Based on the
quality of the derived land-use maps templates were selected
or removed from the database The final set of templates was
used to classify all tiles
C Implementation on a computer cluster
The OSPARK algorithm was applied to all tiles coveringPuerto Rico using the templates database derived using the
procedure described in the previous section After general
preprocessing consisting of preparing the input tiles and
obtaining a good set of templates (T) for the database
OSPARK is run at a computer cluster The cluster hardware
consists of a server with a dual core Intel Xeon CPU (28 GHz)
and 1 GB of RAM The 19 nodes of the cluster each consist of
2 Intel Xeon CPUrsquos and between 4 and 12 GB of RAM which
allows the parallel execution of up to 144 jobs In the current
set up of the algorithm the maximum kernel apothem (W )
was set to 30 pixels which is a trade-off between calculation
time and classification accuracy With this configuration four
tiles can be parallel processed at the cluster The OSPARKalgorithm applied to each tile consists of
1) Loading the proper tile and templates database
2) Parallel execution of SPARK for apothems ranging from
1 to W pixels where W = 30 in this case
3) Running the integration operator that estimates the op-
timal class for each cell based on the stack of similarity
maps and resampling the output from 3 to 60 m cells
using a majority filter of 120 m
In step 3 also ocean and forest reserves are copied from the
Xplorah 2010 land-use map to the OSPARK classification
because the ocean class does not show much dynamics and
the forest reserves class is determined by policy decisionsand zoning documents rather than morphology or reflective
properties of the landscape Therefore it is not feasible to
derive this class by means of remote sensing techniques
D Postprocessing
After all tiles of all four sections are calculated a general
postprocessing routine mosaickes all the classified tiles into
land-use maps of Puerto Rico at 60 m resolution
V RESULTS
A OSPARK results for training tiles
Analysis of the contingency matrices of the classificationof the three tiles shows that the kappa and overall accuracy
of the classification of the training tiles is not always higher
than 66 The producerrsquos and userrsquos accuracy of the individual
classes show that some classes can be retrieved at an accuracy
higher than 66 while others are classified with a lower
accuracy The results vary per training tile In training tile 1
the classes construction mining residential sea beach water
resources and utilities have a producerrsquos and userrsquos accuracy
higher than 05 Other classes show a higher level of confusion
Training tile 2 shows a better result but many classes are not
present in the scene that covers mainly an agricultural and
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 44
forested area Good results were obtained for the classes forest
trade and services residential water resources public and
recreation and rangelands For training tile 3 good results were
obtained for urban classes construction industry residential
public and recreation utilities and infrastructure In addition
good results were also obtained for the non-urban classes
forest agriculture mangroves and swamps sea beach water
resources and rangelandsAn optimal database of templates was derived by trial-and-
error based on the analysis of these three tiles The optimal
database was used to classify the entire Commonwealth of
Puerto Rico
B OSPARK results for all tiles
In approximately one month time all tiles were processed
by the computer cluster (Fig 3) The overall accuracy is 66
and the kappa value is 057 The high figures are however
biased by the large area of sea and forest reserves that are
not taken into account by OSPARK but directly derived from
the Xplorah 2010 land-use map A more detailed analysis
of the accuracy reveals that most classes have a low userrsquos
and producerrsquos accuracy Exceptions are the relatively high
userrsquos and producerrsquos accuracy for the forest and residential
classes Water resources and public- and recreation facilities
can be derived with an acceptable userrsquos accuracy although
their producerrsquos accuracy is low
VI DISCUSSION AND CONCLUSIONS
In this study the feasibility of using a fully automated land-
use classification procedure applied to high resolution remote
sensing images has been investigated A processing chain has
been described for (1) preprocessing the aerial photographs
(2) performing a pre-classification of the blue green red andnear-infrared channels of the orthomosaic based on a decision
tree classification (3) training of the OSPARK algorithm using
three training tiles covering important land-use types and
(4) running the algorithm on a computer cluster in order to
improve the calculation times by parallel processing of the
kernels
Results of the classification procedure were compared with
the Xplorah 2010 land-use classification which has a reported
overall accuracy of 97 Although the results for the individ-
ual training tiles were promising and gave acceptable results
for most land-use classes the application of the algorithm to
the entire Commonwealth of Puerto Rico resulted in a much
lower accuracy for most classes Classes that can be inferred
with an acceptable accuracy using the proposed procedure are
forest residential water resources and public and recreation
The overall accuracy was 66 This value is however biased
by sea and forest reserve classes that were not derived by the
OSPARK classification but were copied from the reference
map
The errors in the classification can be attributed to different
sources The main source of errors is caused by the templates
database that is used Although the templates in the database
gave good results for the three training tiles the results for
the entire Commonwealth of Puerto Rico indicate that the
templates were not representative for all tiles and could not
be extrapolated Further research should focus on a better
training of the template database using statistical or machine
learning techniques It should also be investigated if it is
feasible to classify Puerto Rico with only one representative
set of templates or if a spatial stratification would yield better
classification resultsOther sources of errors could be introduced by the maxi-
mum kernel size which choice is a trade-off between calcu-
lation time and accuracy Furthermore the resolution of 3 m
chosen for the initial land-cover map has an impact on the
detection of homogeneous objects and consequently on the
configuration of objects within a kernel to be classified by
OSPARK This problem is aggravated by the comparison of
the automatically interpreted land-use map with the Xplorah
2010 land-use map which is generated at 15 m resolution by
means of visual interpretation The visual interpretation will
based on human insight generalize areas featuring a salt-and-
pepper structure in the most meaningful land uses covering
larger contiguous areas while the automatic classification will
consider the individual cells as meaningfull contributors to
each template analyzed Examples of such generalizations are
described in [1] Other errors could be introduced by the
histogram matching of the aerial photographs which might
cause a different illumination in the different regions Future
studies should also investigate these causes of inaccuraciesIn general it can be concluded that the automatic derivation
of 18 land-use classes by means of remote sensing techniques
remains a challenge The proposed processing chain however
can contribute to more advanced methods of classification that
can increase the time interval between land-use maps while
reducing the production costs compared to the labor-intensivemanual map production
ACKNOWLEDGMENT
The research presented in this paper is funded by the
Graduate School of Planning University of Puerto Rico in
the frame of the Xplorah project The reference land-use data
were made available by GMT Corp
REFERENCES
[1] G Roman A Castro and E Carreras ldquoGeneration of land-use mapsrequired for the implementation phase of a spatial decision supportsystem for puerto rico Xplorah 2010 land-use maprdquo Geographic MappingTechnologies Corporation San Juan Puerto Rico Tech Rep 2010
[2] J van der Kwast T van de Voorde F Canters I Uljee S van Looyand G Engelen ldquoInferring urban land use using the optimised spatialreclassification kernel (OSPARK)rdquo Environmental Modelling amp Softwarein review
[3] M Barnsley and S Barr ldquoInferring urban land use from satellite sensorimages using kernel-based analysis and classificationrdquo Photogramm Eng
Rem S vol 62 no 8 pp 949ndash958 1996[4] S M de Jong and F van der Meer Remote sensing image analysis
including the spatial domain ser Remote sensing and digital imageprocessing 5 Kluwer academic publishers 2004
[5] J van der Kwast T van de Voorde F Canters G Engelen andC Lavalle ldquoUsing remote sensing derived spatial metrics for the cal-ibration of land-use change modelsrdquo in IEEE Proceedings of the 7th
International Urban Remote Sensing Conference (URS 2009) ShanghaiIEEE 2009
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 24
Fig 1 Flowchart of the OSPARK algorithm The shaded part shows theoriginal SPARK algorithm that is iterated for a range of kernel sizes in theOSPARK algorithm [5]
III THE OSPARK ALGORITHM
The Optimized SPARK (OSPARK) algorithm [2] is a con-
textual reclassifier which is based on the Spatial Reclas-
sification Kernel (SPARK [3]) Contextual reclassifiers are
based on the concept that information captured in neighboring
cells or information about patterns surrounding the pixel of
interest may provide useful supplementary information in the
classification process [4] Previous research [3] has demon-
strated a strong relationship between the spatial structure of
urban areas and its functional characteristics The SPARK
algorithm examines the local spatial patterns of land cover
in a square kernel or moving window and classifies the center
pixel based on the arrangement of adjacent pixels OSPARK
is an extension to SPARK in the sense that it automatically
adapts the kernel size to the spatial variation detected around
the pixel to be classified The classification consists of three
phases [3]
1) Producing a land-cover map using any type of pixel-
based spectral classifier from a remotely sensed image
further referred to as lsquoinitial land-cover maprsquo
2) Defining decision rules based on local spatial patterns
of land cover in typical land-use types
3) Reclassifying the initial land-cover map into land-use
types based on the decision rules of phase 2
Fig 1 shows the flowchart of the OSPARK algorithm The
algorithm derives adjacency event matrices M by counting the
frequency of the pixel-based classes positioned next to each
other as well as diagonally within each template kernel Next
the M-matrices are compared with template (T) matrices thatare derived from kernels that are representative for the land-
use classes to be derived The similarity index is used as
a goodness-of-fit measure
= 1 minus
09830865 minus2
1038389sum1103925=1
1038389sum907317=1
98308010383891103925907317 minus 11039251038389
2(1)
where 10383891103925907317 is the adjacency event in a 907317 by 907317 matrix M 11039251038389 is
the adjacency event in a 907317 by 907317 matrix T which is a template
matrix for land-use class is the total number of adjacency
983154983156983144983151983152983144983151983156983151983155983090983088983089983088
983089983088983147 983160 983089983088983147 983156983145983148983141983155983104 983088983086983091 983149
983113983150983145983156983145983137983148983148983137983150983140‐983139983151983158983141983154 983149983137983152
983089983147 983160 983089983147 983156983145983148983141983155983104 983091 983149
Resampling
Conversion to IDL
983123983120983105983122983115 983148983137983150983140‐983157983155983141983149983137983152
983091983147 983160 983091983147 983156983145983148983141983155983104 983091 983149
Conversion to
PCRaster
983123983120983105983122983115 983148983137983150983140‐983157983155983141983149983137983152
983104 983089983093 983149
983104 983094983088 983149
983104 983090983092983088 983149
983113983150983145983156983145983137983148983148983137983150983140‐983139983151983158983141983154 983149983137983152 983151983142
983156983154983137983145983150983145983150983143 983156983145983148983141983155
983123983120983105983122983115 983148983137983150983140‐983157983155983141983149983137983152 983151983142
983156983154983137983145983150983145983150983143 983156983145983148983141983155
983122983141983142983141983154983141983150983139983141 983148983137983150983140‐983157983155983141983149983137983152
983104 983089983093 983149
983104 983094983088 983149
983104 983090983092983088 983149
Resampling
Reclass
983113983150983145983156983145983137983148983148983137983150983140‐983139983151983158983141983154 983149983137983152
983091983147 983160 983091983147 983156983145983148983141983155983104 983091 983149983124983141983149983152983148983137983156983141
983140983137983156983137983138983137983155983141
983123983120983105983122983115
983137983148983143983151983154983145983156983144983149
Histogram matching
GDAL retile
Mosaick
983126983137983148983145983140983137983156983145983151983150
Sample
templates
Fig 2 Flowchart of the classification procedure
events in the kernel and 907317 is the number of classes in the per-
pixel classified input map can range from 0 to 1 If
equals 0 M is completely different from T while a value
of 1 means that they are identical
OSPARK iteratively calculates the similarity index for ker-
nel sizes with an apothem ie distance from the center pixelto a side of a square kernel from 1 to pixels The resulting
stack consisting of similarity maps is analyzed by an
integration operator which assigns the class that corresponds
with the optimal -value for each pixel The optimal -
value is determined based on two possible cases for the
evolution of with increasing kernel size [2]
1) In the case that local maxima are present the first local
maximum above a user-defined minimum -threshold
value is determined and the corresponding land-use class
is assigned
2) In the case that local maxima are absent the curve
converges to
asymp 1
and the integration operator assignsthe class to the pixel when the -value changes less
than 005 between consecutive iterations and is higher
than the threshold value
The threshold prevents classification of pixels with a too
low -value
The derived land-use map and a map containing the -
value corresponding to the optimal kernel size for each pixel
are the outputs of the algorithm
IV THE PROCESSING CHAIN
Fig 2 shows the workflow for the automatic classification of
the orthorectified aerial photographs of 2009 The procedure
consists of preprocessing building the template database run-ning OSPARK in batch on a computer cluster post-processing
and accuracy assessment
A Preprocessing
First the 1500 orthophoto tiles were in batch converted to
the IDL ENVI image format and resampled to tiles of 1000
by 1000 pixels with 3 m resolution Next each tile containing
blue green red and near-infrared channels was classified
using a decision tree classification The decision tree classifier
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 34
Legend
NATURAL
FOREST
AGRICULTURE
CONSTRUCTION
MINING
INDUSTRIAL
HIGH-DENSITYTRA DE AND SERVICES
HIGH-DENSITY RESIDENTIAL
FORESTRESERVES
MANGROVES AND SWAMPS
SEA
BEACH
CORALREEF
WATER RESOURCES
PUBLIC AND RECREATION
UTILITIES
INFRASTRUCTURE
ROCKYCLIFF S AND SHELVES
RANGELANDS
LOW-DENSITY TRADE AND SERVICES
LOW-DENSITY RESIDENTIAL
0 20 4010Kilometers
1
2
3
Fig 3 Training tiles for building the OSPARK template database 1 =urbanized area (San Juan) 2 = naturalrural area (around Bosque Estatal deMonte Guilarte) 3 = urbanized area (Mayaguez) The background map showsthe OSPARK classification result
is an unsupervised classification method that performs a multi-
stage classification by using a series of binary decisions inorder to cluster pixels This procedure resulted in 25 classes
3 m was considered as the most optimal resolution for the
initial land-cover map as the objects could be clearly defined
at this resolution while noise introduced by unnecessary
spatial detail was avoided The initial land-cover map was
retiled to tiles with 3000 by 3000 pixels and converted to
the PCRaster format which is the input for the OSPARK
algorithm Open-source utilities distributed with the Geospatial
Data Abstraction Library (GDAL httpwwwgdalorg) were
used to perform this The size of the tiles was considered as
optimal since small tiles would result in many missing values
after the OSPARK classification while larger tiles could causethe system to run out of memory Separate tiles were calculated
to cover the tile edges that will have missing values after the
OSPARK classification In total 178 tiles of 3000 by 3000
pixels 150 tiles of 100 by 3000 (row by columns) and 166 tiles
of 3000 by 100 were used to classify the entire Commonwealth
of Puerto Rico
B Building the template database
The OSPARK algorithm needs a database of representative
template matrices For this purpose three 3000 by 3000 tiles
were selected (Fig 3)
These training tiles were selected in order to include the
most important land-use classes involved in urban dynamics
but also to represent natural and rural land-use classes The
center coordinates of the template kernels were derived by
stratified random sampling of 50 points within each class of
the land-use map The same procedure was followed to derive
an independent set of pixels for evaluation of the contextual
classification of the tiles
In order to check the quality of the selected templates and
their transferability to different areas different combinations
of templates have been used in the OSPARK classifications
of the three tiles The resulting maps were evaluated using
contingency matrices with the independent reference data
sampled from the Xplorah 2010 land-use map Based on the
quality of the derived land-use maps templates were selected
or removed from the database The final set of templates was
used to classify all tiles
C Implementation on a computer cluster
The OSPARK algorithm was applied to all tiles coveringPuerto Rico using the templates database derived using the
procedure described in the previous section After general
preprocessing consisting of preparing the input tiles and
obtaining a good set of templates (T) for the database
OSPARK is run at a computer cluster The cluster hardware
consists of a server with a dual core Intel Xeon CPU (28 GHz)
and 1 GB of RAM The 19 nodes of the cluster each consist of
2 Intel Xeon CPUrsquos and between 4 and 12 GB of RAM which
allows the parallel execution of up to 144 jobs In the current
set up of the algorithm the maximum kernel apothem (W )
was set to 30 pixels which is a trade-off between calculation
time and classification accuracy With this configuration four
tiles can be parallel processed at the cluster The OSPARKalgorithm applied to each tile consists of
1) Loading the proper tile and templates database
2) Parallel execution of SPARK for apothems ranging from
1 to W pixels where W = 30 in this case
3) Running the integration operator that estimates the op-
timal class for each cell based on the stack of similarity
maps and resampling the output from 3 to 60 m cells
using a majority filter of 120 m
In step 3 also ocean and forest reserves are copied from the
Xplorah 2010 land-use map to the OSPARK classification
because the ocean class does not show much dynamics and
the forest reserves class is determined by policy decisionsand zoning documents rather than morphology or reflective
properties of the landscape Therefore it is not feasible to
derive this class by means of remote sensing techniques
D Postprocessing
After all tiles of all four sections are calculated a general
postprocessing routine mosaickes all the classified tiles into
land-use maps of Puerto Rico at 60 m resolution
V RESULTS
A OSPARK results for training tiles
Analysis of the contingency matrices of the classificationof the three tiles shows that the kappa and overall accuracy
of the classification of the training tiles is not always higher
than 66 The producerrsquos and userrsquos accuracy of the individual
classes show that some classes can be retrieved at an accuracy
higher than 66 while others are classified with a lower
accuracy The results vary per training tile In training tile 1
the classes construction mining residential sea beach water
resources and utilities have a producerrsquos and userrsquos accuracy
higher than 05 Other classes show a higher level of confusion
Training tile 2 shows a better result but many classes are not
present in the scene that covers mainly an agricultural and
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 44
forested area Good results were obtained for the classes forest
trade and services residential water resources public and
recreation and rangelands For training tile 3 good results were
obtained for urban classes construction industry residential
public and recreation utilities and infrastructure In addition
good results were also obtained for the non-urban classes
forest agriculture mangroves and swamps sea beach water
resources and rangelandsAn optimal database of templates was derived by trial-and-
error based on the analysis of these three tiles The optimal
database was used to classify the entire Commonwealth of
Puerto Rico
B OSPARK results for all tiles
In approximately one month time all tiles were processed
by the computer cluster (Fig 3) The overall accuracy is 66
and the kappa value is 057 The high figures are however
biased by the large area of sea and forest reserves that are
not taken into account by OSPARK but directly derived from
the Xplorah 2010 land-use map A more detailed analysis
of the accuracy reveals that most classes have a low userrsquos
and producerrsquos accuracy Exceptions are the relatively high
userrsquos and producerrsquos accuracy for the forest and residential
classes Water resources and public- and recreation facilities
can be derived with an acceptable userrsquos accuracy although
their producerrsquos accuracy is low
VI DISCUSSION AND CONCLUSIONS
In this study the feasibility of using a fully automated land-
use classification procedure applied to high resolution remote
sensing images has been investigated A processing chain has
been described for (1) preprocessing the aerial photographs
(2) performing a pre-classification of the blue green red andnear-infrared channels of the orthomosaic based on a decision
tree classification (3) training of the OSPARK algorithm using
three training tiles covering important land-use types and
(4) running the algorithm on a computer cluster in order to
improve the calculation times by parallel processing of the
kernels
Results of the classification procedure were compared with
the Xplorah 2010 land-use classification which has a reported
overall accuracy of 97 Although the results for the individ-
ual training tiles were promising and gave acceptable results
for most land-use classes the application of the algorithm to
the entire Commonwealth of Puerto Rico resulted in a much
lower accuracy for most classes Classes that can be inferred
with an acceptable accuracy using the proposed procedure are
forest residential water resources and public and recreation
The overall accuracy was 66 This value is however biased
by sea and forest reserve classes that were not derived by the
OSPARK classification but were copied from the reference
map
The errors in the classification can be attributed to different
sources The main source of errors is caused by the templates
database that is used Although the templates in the database
gave good results for the three training tiles the results for
the entire Commonwealth of Puerto Rico indicate that the
templates were not representative for all tiles and could not
be extrapolated Further research should focus on a better
training of the template database using statistical or machine
learning techniques It should also be investigated if it is
feasible to classify Puerto Rico with only one representative
set of templates or if a spatial stratification would yield better
classification resultsOther sources of errors could be introduced by the maxi-
mum kernel size which choice is a trade-off between calcu-
lation time and accuracy Furthermore the resolution of 3 m
chosen for the initial land-cover map has an impact on the
detection of homogeneous objects and consequently on the
configuration of objects within a kernel to be classified by
OSPARK This problem is aggravated by the comparison of
the automatically interpreted land-use map with the Xplorah
2010 land-use map which is generated at 15 m resolution by
means of visual interpretation The visual interpretation will
based on human insight generalize areas featuring a salt-and-
pepper structure in the most meaningful land uses covering
larger contiguous areas while the automatic classification will
consider the individual cells as meaningfull contributors to
each template analyzed Examples of such generalizations are
described in [1] Other errors could be introduced by the
histogram matching of the aerial photographs which might
cause a different illumination in the different regions Future
studies should also investigate these causes of inaccuraciesIn general it can be concluded that the automatic derivation
of 18 land-use classes by means of remote sensing techniques
remains a challenge The proposed processing chain however
can contribute to more advanced methods of classification that
can increase the time interval between land-use maps while
reducing the production costs compared to the labor-intensivemanual map production
ACKNOWLEDGMENT
The research presented in this paper is funded by the
Graduate School of Planning University of Puerto Rico in
the frame of the Xplorah project The reference land-use data
were made available by GMT Corp
REFERENCES
[1] G Roman A Castro and E Carreras ldquoGeneration of land-use mapsrequired for the implementation phase of a spatial decision supportsystem for puerto rico Xplorah 2010 land-use maprdquo Geographic MappingTechnologies Corporation San Juan Puerto Rico Tech Rep 2010
[2] J van der Kwast T van de Voorde F Canters I Uljee S van Looyand G Engelen ldquoInferring urban land use using the optimised spatialreclassification kernel (OSPARK)rdquo Environmental Modelling amp Softwarein review
[3] M Barnsley and S Barr ldquoInferring urban land use from satellite sensorimages using kernel-based analysis and classificationrdquo Photogramm Eng
Rem S vol 62 no 8 pp 949ndash958 1996[4] S M de Jong and F van der Meer Remote sensing image analysis
including the spatial domain ser Remote sensing and digital imageprocessing 5 Kluwer academic publishers 2004
[5] J van der Kwast T van de Voorde F Canters G Engelen andC Lavalle ldquoUsing remote sensing derived spatial metrics for the cal-ibration of land-use change modelsrdquo in IEEE Proceedings of the 7th
International Urban Remote Sensing Conference (URS 2009) ShanghaiIEEE 2009
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 34
Legend
NATURAL
FOREST
AGRICULTURE
CONSTRUCTION
MINING
INDUSTRIAL
HIGH-DENSITYTRA DE AND SERVICES
HIGH-DENSITY RESIDENTIAL
FORESTRESERVES
MANGROVES AND SWAMPS
SEA
BEACH
CORALREEF
WATER RESOURCES
PUBLIC AND RECREATION
UTILITIES
INFRASTRUCTURE
ROCKYCLIFF S AND SHELVES
RANGELANDS
LOW-DENSITY TRADE AND SERVICES
LOW-DENSITY RESIDENTIAL
0 20 4010Kilometers
1
2
3
Fig 3 Training tiles for building the OSPARK template database 1 =urbanized area (San Juan) 2 = naturalrural area (around Bosque Estatal deMonte Guilarte) 3 = urbanized area (Mayaguez) The background map showsthe OSPARK classification result
is an unsupervised classification method that performs a multi-
stage classification by using a series of binary decisions inorder to cluster pixels This procedure resulted in 25 classes
3 m was considered as the most optimal resolution for the
initial land-cover map as the objects could be clearly defined
at this resolution while noise introduced by unnecessary
spatial detail was avoided The initial land-cover map was
retiled to tiles with 3000 by 3000 pixels and converted to
the PCRaster format which is the input for the OSPARK
algorithm Open-source utilities distributed with the Geospatial
Data Abstraction Library (GDAL httpwwwgdalorg) were
used to perform this The size of the tiles was considered as
optimal since small tiles would result in many missing values
after the OSPARK classification while larger tiles could causethe system to run out of memory Separate tiles were calculated
to cover the tile edges that will have missing values after the
OSPARK classification In total 178 tiles of 3000 by 3000
pixels 150 tiles of 100 by 3000 (row by columns) and 166 tiles
of 3000 by 100 were used to classify the entire Commonwealth
of Puerto Rico
B Building the template database
The OSPARK algorithm needs a database of representative
template matrices For this purpose three 3000 by 3000 tiles
were selected (Fig 3)
These training tiles were selected in order to include the
most important land-use classes involved in urban dynamics
but also to represent natural and rural land-use classes The
center coordinates of the template kernels were derived by
stratified random sampling of 50 points within each class of
the land-use map The same procedure was followed to derive
an independent set of pixels for evaluation of the contextual
classification of the tiles
In order to check the quality of the selected templates and
their transferability to different areas different combinations
of templates have been used in the OSPARK classifications
of the three tiles The resulting maps were evaluated using
contingency matrices with the independent reference data
sampled from the Xplorah 2010 land-use map Based on the
quality of the derived land-use maps templates were selected
or removed from the database The final set of templates was
used to classify all tiles
C Implementation on a computer cluster
The OSPARK algorithm was applied to all tiles coveringPuerto Rico using the templates database derived using the
procedure described in the previous section After general
preprocessing consisting of preparing the input tiles and
obtaining a good set of templates (T) for the database
OSPARK is run at a computer cluster The cluster hardware
consists of a server with a dual core Intel Xeon CPU (28 GHz)
and 1 GB of RAM The 19 nodes of the cluster each consist of
2 Intel Xeon CPUrsquos and between 4 and 12 GB of RAM which
allows the parallel execution of up to 144 jobs In the current
set up of the algorithm the maximum kernel apothem (W )
was set to 30 pixels which is a trade-off between calculation
time and classification accuracy With this configuration four
tiles can be parallel processed at the cluster The OSPARKalgorithm applied to each tile consists of
1) Loading the proper tile and templates database
2) Parallel execution of SPARK for apothems ranging from
1 to W pixels where W = 30 in this case
3) Running the integration operator that estimates the op-
timal class for each cell based on the stack of similarity
maps and resampling the output from 3 to 60 m cells
using a majority filter of 120 m
In step 3 also ocean and forest reserves are copied from the
Xplorah 2010 land-use map to the OSPARK classification
because the ocean class does not show much dynamics and
the forest reserves class is determined by policy decisionsand zoning documents rather than morphology or reflective
properties of the landscape Therefore it is not feasible to
derive this class by means of remote sensing techniques
D Postprocessing
After all tiles of all four sections are calculated a general
postprocessing routine mosaickes all the classified tiles into
land-use maps of Puerto Rico at 60 m resolution
V RESULTS
A OSPARK results for training tiles
Analysis of the contingency matrices of the classificationof the three tiles shows that the kappa and overall accuracy
of the classification of the training tiles is not always higher
than 66 The producerrsquos and userrsquos accuracy of the individual
classes show that some classes can be retrieved at an accuracy
higher than 66 while others are classified with a lower
accuracy The results vary per training tile In training tile 1
the classes construction mining residential sea beach water
resources and utilities have a producerrsquos and userrsquos accuracy
higher than 05 Other classes show a higher level of confusion
Training tile 2 shows a better result but many classes are not
present in the scene that covers mainly an agricultural and
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 44
forested area Good results were obtained for the classes forest
trade and services residential water resources public and
recreation and rangelands For training tile 3 good results were
obtained for urban classes construction industry residential
public and recreation utilities and infrastructure In addition
good results were also obtained for the non-urban classes
forest agriculture mangroves and swamps sea beach water
resources and rangelandsAn optimal database of templates was derived by trial-and-
error based on the analysis of these three tiles The optimal
database was used to classify the entire Commonwealth of
Puerto Rico
B OSPARK results for all tiles
In approximately one month time all tiles were processed
by the computer cluster (Fig 3) The overall accuracy is 66
and the kappa value is 057 The high figures are however
biased by the large area of sea and forest reserves that are
not taken into account by OSPARK but directly derived from
the Xplorah 2010 land-use map A more detailed analysis
of the accuracy reveals that most classes have a low userrsquos
and producerrsquos accuracy Exceptions are the relatively high
userrsquos and producerrsquos accuracy for the forest and residential
classes Water resources and public- and recreation facilities
can be derived with an acceptable userrsquos accuracy although
their producerrsquos accuracy is low
VI DISCUSSION AND CONCLUSIONS
In this study the feasibility of using a fully automated land-
use classification procedure applied to high resolution remote
sensing images has been investigated A processing chain has
been described for (1) preprocessing the aerial photographs
(2) performing a pre-classification of the blue green red andnear-infrared channels of the orthomosaic based on a decision
tree classification (3) training of the OSPARK algorithm using
three training tiles covering important land-use types and
(4) running the algorithm on a computer cluster in order to
improve the calculation times by parallel processing of the
kernels
Results of the classification procedure were compared with
the Xplorah 2010 land-use classification which has a reported
overall accuracy of 97 Although the results for the individ-
ual training tiles were promising and gave acceptable results
for most land-use classes the application of the algorithm to
the entire Commonwealth of Puerto Rico resulted in a much
lower accuracy for most classes Classes that can be inferred
with an acceptable accuracy using the proposed procedure are
forest residential water resources and public and recreation
The overall accuracy was 66 This value is however biased
by sea and forest reserve classes that were not derived by the
OSPARK classification but were copied from the reference
map
The errors in the classification can be attributed to different
sources The main source of errors is caused by the templates
database that is used Although the templates in the database
gave good results for the three training tiles the results for
the entire Commonwealth of Puerto Rico indicate that the
templates were not representative for all tiles and could not
be extrapolated Further research should focus on a better
training of the template database using statistical or machine
learning techniques It should also be investigated if it is
feasible to classify Puerto Rico with only one representative
set of templates or if a spatial stratification would yield better
classification resultsOther sources of errors could be introduced by the maxi-
mum kernel size which choice is a trade-off between calcu-
lation time and accuracy Furthermore the resolution of 3 m
chosen for the initial land-cover map has an impact on the
detection of homogeneous objects and consequently on the
configuration of objects within a kernel to be classified by
OSPARK This problem is aggravated by the comparison of
the automatically interpreted land-use map with the Xplorah
2010 land-use map which is generated at 15 m resolution by
means of visual interpretation The visual interpretation will
based on human insight generalize areas featuring a salt-and-
pepper structure in the most meaningful land uses covering
larger contiguous areas while the automatic classification will
consider the individual cells as meaningfull contributors to
each template analyzed Examples of such generalizations are
described in [1] Other errors could be introduced by the
histogram matching of the aerial photographs which might
cause a different illumination in the different regions Future
studies should also investigate these causes of inaccuraciesIn general it can be concluded that the automatic derivation
of 18 land-use classes by means of remote sensing techniques
remains a challenge The proposed processing chain however
can contribute to more advanced methods of classification that
can increase the time interval between land-use maps while
reducing the production costs compared to the labor-intensivemanual map production
ACKNOWLEDGMENT
The research presented in this paper is funded by the
Graduate School of Planning University of Puerto Rico in
the frame of the Xplorah project The reference land-use data
were made available by GMT Corp
REFERENCES
[1] G Roman A Castro and E Carreras ldquoGeneration of land-use mapsrequired for the implementation phase of a spatial decision supportsystem for puerto rico Xplorah 2010 land-use maprdquo Geographic MappingTechnologies Corporation San Juan Puerto Rico Tech Rep 2010
[2] J van der Kwast T van de Voorde F Canters I Uljee S van Looyand G Engelen ldquoInferring urban land use using the optimised spatialreclassification kernel (OSPARK)rdquo Environmental Modelling amp Softwarein review
[3] M Barnsley and S Barr ldquoInferring urban land use from satellite sensorimages using kernel-based analysis and classificationrdquo Photogramm Eng
Rem S vol 62 no 8 pp 949ndash958 1996[4] S M de Jong and F van der Meer Remote sensing image analysis
including the spatial domain ser Remote sensing and digital imageprocessing 5 Kluwer academic publishers 2004
[5] J van der Kwast T van de Voorde F Canters G Engelen andC Lavalle ldquoUsing remote sensing derived spatial metrics for the cal-ibration of land-use change modelsrdquo in IEEE Proceedings of the 7th
International Urban Remote Sensing Conference (URS 2009) ShanghaiIEEE 2009
7262019 Automatic Generation of Land-Use Maps
httpslidepdfcomreaderfullautomatic-generation-of-land-use-maps 44
forested area Good results were obtained for the classes forest
trade and services residential water resources public and
recreation and rangelands For training tile 3 good results were
obtained for urban classes construction industry residential
public and recreation utilities and infrastructure In addition
good results were also obtained for the non-urban classes
forest agriculture mangroves and swamps sea beach water
resources and rangelandsAn optimal database of templates was derived by trial-and-
error based on the analysis of these three tiles The optimal
database was used to classify the entire Commonwealth of
Puerto Rico
B OSPARK results for all tiles
In approximately one month time all tiles were processed
by the computer cluster (Fig 3) The overall accuracy is 66
and the kappa value is 057 The high figures are however
biased by the large area of sea and forest reserves that are
not taken into account by OSPARK but directly derived from
the Xplorah 2010 land-use map A more detailed analysis
of the accuracy reveals that most classes have a low userrsquos
and producerrsquos accuracy Exceptions are the relatively high
userrsquos and producerrsquos accuracy for the forest and residential
classes Water resources and public- and recreation facilities
can be derived with an acceptable userrsquos accuracy although
their producerrsquos accuracy is low
VI DISCUSSION AND CONCLUSIONS
In this study the feasibility of using a fully automated land-
use classification procedure applied to high resolution remote
sensing images has been investigated A processing chain has
been described for (1) preprocessing the aerial photographs
(2) performing a pre-classification of the blue green red andnear-infrared channels of the orthomosaic based on a decision
tree classification (3) training of the OSPARK algorithm using
three training tiles covering important land-use types and
(4) running the algorithm on a computer cluster in order to
improve the calculation times by parallel processing of the
kernels
Results of the classification procedure were compared with
the Xplorah 2010 land-use classification which has a reported
overall accuracy of 97 Although the results for the individ-
ual training tiles were promising and gave acceptable results
for most land-use classes the application of the algorithm to
the entire Commonwealth of Puerto Rico resulted in a much
lower accuracy for most classes Classes that can be inferred
with an acceptable accuracy using the proposed procedure are
forest residential water resources and public and recreation
The overall accuracy was 66 This value is however biased
by sea and forest reserve classes that were not derived by the
OSPARK classification but were copied from the reference
map
The errors in the classification can be attributed to different
sources The main source of errors is caused by the templates
database that is used Although the templates in the database
gave good results for the three training tiles the results for
the entire Commonwealth of Puerto Rico indicate that the
templates were not representative for all tiles and could not
be extrapolated Further research should focus on a better
training of the template database using statistical or machine
learning techniques It should also be investigated if it is
feasible to classify Puerto Rico with only one representative
set of templates or if a spatial stratification would yield better
classification resultsOther sources of errors could be introduced by the maxi-
mum kernel size which choice is a trade-off between calcu-
lation time and accuracy Furthermore the resolution of 3 m
chosen for the initial land-cover map has an impact on the
detection of homogeneous objects and consequently on the
configuration of objects within a kernel to be classified by
OSPARK This problem is aggravated by the comparison of
the automatically interpreted land-use map with the Xplorah
2010 land-use map which is generated at 15 m resolution by
means of visual interpretation The visual interpretation will
based on human insight generalize areas featuring a salt-and-
pepper structure in the most meaningful land uses covering
larger contiguous areas while the automatic classification will
consider the individual cells as meaningfull contributors to
each template analyzed Examples of such generalizations are
described in [1] Other errors could be introduced by the
histogram matching of the aerial photographs which might
cause a different illumination in the different regions Future
studies should also investigate these causes of inaccuraciesIn general it can be concluded that the automatic derivation
of 18 land-use classes by means of remote sensing techniques
remains a challenge The proposed processing chain however
can contribute to more advanced methods of classification that
can increase the time interval between land-use maps while
reducing the production costs compared to the labor-intensivemanual map production
ACKNOWLEDGMENT
The research presented in this paper is funded by the
Graduate School of Planning University of Puerto Rico in
the frame of the Xplorah project The reference land-use data
were made available by GMT Corp
REFERENCES
[1] G Roman A Castro and E Carreras ldquoGeneration of land-use mapsrequired for the implementation phase of a spatial decision supportsystem for puerto rico Xplorah 2010 land-use maprdquo Geographic MappingTechnologies Corporation San Juan Puerto Rico Tech Rep 2010
[2] J van der Kwast T van de Voorde F Canters I Uljee S van Looyand G Engelen ldquoInferring urban land use using the optimised spatialreclassification kernel (OSPARK)rdquo Environmental Modelling amp Softwarein review
[3] M Barnsley and S Barr ldquoInferring urban land use from satellite sensorimages using kernel-based analysis and classificationrdquo Photogramm Eng
Rem S vol 62 no 8 pp 949ndash958 1996[4] S M de Jong and F van der Meer Remote sensing image analysis
including the spatial domain ser Remote sensing and digital imageprocessing 5 Kluwer academic publishers 2004
[5] J van der Kwast T van de Voorde F Canters G Engelen andC Lavalle ldquoUsing remote sensing derived spatial metrics for the cal-ibration of land-use change modelsrdquo in IEEE Proceedings of the 7th
International Urban Remote Sensing Conference (URS 2009) ShanghaiIEEE 2009