Evaluation of supervised land-cover classification by PALSAR polarimetric interferometry

Masato Ohki and Masanobu ShimadaEarth Observation Research Center, Japan Aerospace Exploration Agency

Outline Background

Polarimetric interferometry (PolInSAR) PALSAR PolInSAR data

Methods and data

Result: Land-cover classification by PALSAR PolInSAR

Discussion Advantage of PolInSAR for LC classification Comparison between classification methods Comparison with optical sensor data

Conclusion and Future work

BACKGROUND

PALSAR polarimetry data PLR (quad-PoLaRimetric mode) Specification:

Off-nadir angle: ≤ 26.1° Ground resolution: ~25m (at 21.5°) Swath width: ~35km (at 21.5°) Capable of interferometry

(minimum temporal distance: 46 days) PLR data coverage (2006-2011)

PALSAR

ALOS

ALOS-2

PALSAR-2

rm

rs

PALSAR Polarimetric Interferometry (PolInSAR)

Issue: single satellite-> repeat-pass interferometry Various spatial distance (0.0~2.5km) Long temporal distance (≥46 days)

-> Application?

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Master

Slave

repeat pass

PolInSARCoherency

matrix

The quake hit Tsukuba Space Center

What can we do for disasterprevention/mitigation?

3.11 Earthquake

Overview of this study Feasibility study on land-cover (LC) monitoring by PALSAR

7 classes supervised LC classification by PALSAR PolInSAR data Accuracy evaluation Comparison between four cases of datasets:

(1) Quad-PolInSAR(2) Dual-PolInSAR(3) Quad-PolSAR(4) Dual-PolSAR

Comparison between classification methods: Wishart SVM

Comparison with other LC product ALOS LC product (optical)

METHODS AND DATA

Test data PALSAR data used in this study

#1 (PLR) #2 (PLR) Optical (AVNIR-2)

# Mode Polarization Off-nadir Path/Row Obs. date12 PLR HH, HV, VH, VV 21.5° 400/710 02 APR 2007

18 MAY 2007345

FBD HH, HV 34.3° 404/700-71009 JUN 200725 JUL 200709 SEP 2007

HH-VVHV

HH+VV(Pauli)

Tsukuba city (36.05˚N,140.10˚E)

NARITA Int’l Airport(35.77˚N,140.39˚E)

Truth LC data Truth land-cover data was made by interpreting:

Land-use 100m mesh data (2006) ©GSI, Japan Optical images (ALOS/AVNIR-2)

Mode Pointing Path/Row Observation date Cloud cover (auto)

OBS 0.0° 67/2870-2880 15/05/200716/08/2007

30-40%0-10%

100m mesh land-use, 2006 ©GSI, Japan (11 classes)

AVNIR-2 image(15 MAY 2007)

Truth data(105 polygons, 8200 samples)

Training datafor classification(4100 samples)

Truth datafor evaluation(4100 samples)

WaterPaddyCropGrassForestUrbanBare

LatLon

AzRg

Class definitionclass# Description

1 Water2 Paddy (rice)

3 Crop field (incl. vegetable, wheat, etc.)

4 Grass (incl. golf course)5 Forest6 Urban (built-up area)7 Bare surface (incl. airstrip, paved area)

Reference data(105 polygons, 8200 samples)

WaterPaddyCropGrassForestUrbanBare

Ground photographs (Tsukuba city, 09 JUN 2009)#2 Paddy #4 Grass#3 Crop #7 Bare

Processing Procedure1. Pre-processing

(imaging, pol. calibration and interferometry) Processor: SIGMA-SAR (by Dr. Shimada)

2. Classification Compared two classification

methods: Wishart classifier and SVM Processor: developed in this study

3. Post-processing(ortho-rectification and geo-coding)

Processor: SIGMA-SAR (by Dr. Shimada) Resolution of the classification map: 60m

Generate SLCPol. CalibrationCo-registration

Slope correction (option)Pol. filtering (option)

Classification(Wishart or SVM)

Ortho-rectification(geo-coding)

DEM

Final classification map

PALSAR L1.0(master)

PALSAR L1.0(Slave)

Trainingdataset

Classifier(1) – Wishart Classifier Maximum likelihood approach assuming that the scattering matrix

follows a complex Wishart distribution function (Lee et al., 1994, 1999)

The pixel is assigned to the class minimizing the distance measure between the pixel and the training class

Scattering matrix for the Wishart classifier

Dataset Quad-PolInSAR(6x6 matrix)

Dual-PolInSAR(4x4 matrix)

Quad-PolSAR(3x3 matrix)

Dual-PolSAR(2x2 matrix)

Matrix

m: masters: slave

T

VVs

HVs

HHs

VVm

HVm

HHm

S

S

S

S

S

S

*666

6

2

2

kkC

k

T

HVs

HHs

HVm

HHm

S

S

S

S

*444

4

kkC

k

T

VV

HV

HH

S

S

S

*333

3 2

kkC

k

T

HV

HH

S

S

*222

2

kkC

k

(master data) (master data)

Classifier(2) – Support Vector Machine (SVM) Margin maximization approach discriminating a class from other classes

in the higher dimensional space(Fukuda and Hirosawa, 2000 for PolSAR data; Shimoni et al., 2009 for PolInSAR data; the SVM core routine is distributed by Chen & Lin, 2005)

Feature parameters for the SVMDataset Quad-PolInSAR Dual-PolInSAR Quad-PolSAR Dual-PolSAR

Master

Amplitude HH, HV, VV,HH+VV, HH-VV HH, HV HH, HV, VV,

HH+VV, HH-VV HH, HV

C.-P. parameters* H, α, A - H, α, A -Polarimetric coherence

HH/HV, HH/VV, HV/VV HH/HV HH/HV, HH/VV,

HV/VV HH/HV

SlaveAmplitude HH, HV, VV,

HH+VV, HH-VV HH, HV - -

C.-P. parameters* H, α, A - - -

Interferometric coherence(master/slave)

HH/HH, HV/HV, VV/VV,

HH+VV/HH+VV, HH-VV/HH-VV

HH/HH, HV/HV - -

Total number of features 24 7 11 3

*The Cloude-Pottier decomposition (Cloude & Pottier, 1996; Pottier 1998)

RESULTS AND DISCUSSION

Classification result (method: SVM)Quad-PolInSAR Dual-PolInSAR Quad-PolSAR Dual-PolSAR

WaterPaddyCrop

GrassForest

UrbanBare

Comparison with optical image Quad-PolInSAR Optical image(ALOS/AVNIR-2)

WaterPaddyCropGrassForestUrbanBare

Classification result (method: Wishart)Quad-PolInSAR Dual-PolInSAR Quad-PolSAR Dual-PolSAR

WaterPaddyCrop

GrassForest

UrbanBare

Comparison of SVM and Wishart Quad-PolInSAR(SVM) Quad-PolInSAR(Wishart)

WaterPaddyCropGrassForestUrbanBare

Evaluation result (confusion matrices)

LC # 1 2 3 4 5 6 7 U.A.1 1527 19 74 10 1 0 66 89.98

2 24 741 32 26 0 0 14 88.53

3 0 1 796 213 3 24 2 76.61

4 0 0 11 112 1 0 67 58.64

5 0 0 80 1 1055 96 0 85.63

6 0 0 149 0 150 2773 0 90.27

7 0 0 2 5 0 2 64 87.67

P.A. 98.45 97.37 69.58 30.52 87.19 95.79 30.05 86.82

Quad-PolInSAR (method: SVM) Quad-PolInSAR (method: Wishart)

Dual-PolInSAR (method: SVM) Quad-PolSAR (method: SVM)

LC# 1:water 2:paddy 3:crop 4:grass 5:forest 6:urban 7:bareU.A.=user’s accuracy(%) P.A.=producer’s accuracy (%) Values in Blue=Overall accuracy(%)

LC # 1 2 3 4 5 6 7 U.A.1 1361 0 5 13 0 0 14 97.70

2 30 668 7 4 0 2 10 92.65

3 0 0 192 9 8 78 0 66.90

4 0 4 698 255 0 42 0 25.53

5 0 0 137 0 1199 1753 0 38.82

6 0 0 36 0 2 1019 0 96.40

7 160 89 69 86 1 1 189 31.76

P.A. 87.75 87.78 16.78 69.48 99.09 35.20 88.73 59.98

LC # 1 2 3 4 5 6 7 U.A.1 1551 266 144 96 1 0 205 68.54

2 0 495 56 14 0 1 4 86.84

3 0 0 705 251 1 41 4 70.36

4 0 0 0 0 0 0 0 0.00

5 0 0 95 0 932 93 0 83.21

6 0 0 144 6 276 2760 0 86.63

7 0 0 0 0 0 0 0 0.00

P.A. 100.00

65.05 61.63 0.00 77.02 95.34 0.00 79.14

LC # 1 2 3 4 5 6 7 U.A.1 1446 62 57 45 1 0 77 85.66

2 105 699 66 130 0 13 136 60.84

3 0 0 739 184 4 64 0 74.57

4 0 0 0 0 0 0 0 0.00

5 0 0 67 2 1095 449 0 67.89

6 0 0 215 6 110 2369 0 87.74

7 0 0 0 0 0 0 0 0.00

P.A. 93.23 91.85 64.60 0.00 90.50 81.83 0.00 77.98

Evaluation result – summary Method: SVM

Method: Wishart

Dataset Quad-PolInSAR Dual-PolInSAR Quad-Pol Dual-PolPolarization

(m):master (s):slaveHH, HV, VV (m)HH, HV, VV (s)

HH, HV (m)HH, HV (s)

HH, HV, VV(m)

HH, HV(m)

Overall Accuracy 86.8 79.1 78.0 66.2Kappa coefficient 0.830 0.727 0.719 0.543Calc. time (sec)* 329 206 272 197

Dataset Quad-PolInSAR Dual-PolInSAR Quad-Pol Dual-PolPolarization

(m):master (s):slaveHH, HV, VV (m)HH, HV, VV (s)

HH, HV (m)HH, HV (s)

HH, HV, VV(m)

HH, HV(m)

Overall Accuracy 60.0 57.5 56.3 53.6Kappa coefficient 0.526 0.498 0.484 0.453Calc. time (sec)* 21.6 9.43 5.35 6.72

> > >

> > >*Calculation time: CPU elapsed time for training and classifying

Detail – Urban area urban area = high coherence

-> PolInSAR effectiveness for discriminating urban

Quad-PolInSAR (SVM) Quad-PolSAR (SVM)

Optical

Urban area

Coherence(HH-VVHVHH+VV)Amplitude

WaterPaddyCropGrassForestUrbanBare

Urban area Urban area?

Detail – Paddy paddy area = lower coherence

-> PolInSAR effectiveness for detecting paddy areas

Quad-PolInSAR (SVM) Quad-PolSAR (SVM)

Coherence(HH-VVHVHH+VV)Amplitude Optical

WaterPaddyCropGrassForestUrbanBare

Paddy areaoverestimated

Paddy

Paddy

Comparison of classification methods Some LC types (esp. urban) can have various scattering mechanism Linear classifier (e.g. Wishart)

Assuming a single scattering mechanism for each class Non-linear or non-parametric classifier (e.g. SVM)

More robust for LC types which have various scattering mechanisms

Quad-PolInSAR (SVM) Quad-PolInSAR (Wishart) Optical

Urban areamisclassified

as Forest

Crop fieldsmisclassified

as Grass

Grassmisclassified

as Bare

ALOS Land-cover product (by the optical sensor) Available at http://www.eorc.jaxa.jp/ALOS/lulc/lulc_jindex.htm (free) Current version: ver. 11.02 (released on Feb 2011) Classification method:

decision tree of multi-seasonal optical sensor images Coverage: Japan area No. of classes: 10 Resolution: 30m Accuracy: 87%

(evaluation result)

ALOS LC product (optical)

Comparison with ALOS LC product PolInSAR (this study) Optical (ALOS LC product)

WaterPaddyCropGrassForestUrbanBare

Comparison with ALOS LC product Advantage of PolInSAR classification:

Precise detection ofForest, Urban, Bare and Water

PolInSAR (this study) Optical (ALOS LC) AVNIR-2 image

WaterPaddyCropGrassForestUrbanBare

Small urban areamisclassified as

Forest

Bare groundsmisclassified

as Water

Comparison with ALOS LC product Advantage of optical classification

Precise detection of low vegetation (Paddy, Crop and Grass)

PolInSAR (this study) Optical (ALOS LC) AVNIR-2 image

WaterPaddyCropGrassForestUrbanBare

Grass areamisclassified

as Crop

Paddymisclassified

as Crop

Summary of results Comparison of datasets

Accuracy: Quad-PolInSAR > Dual-PolInSAR > Quad-Pol > Dual-Pol Interferometric coherence plays important roles for discriminating

LC types which have confusing scattering mechanisms Comparison of classification methods:

Accuracy: SVM > Wishart Computation Speed: Wishart > SVM Non-linear classifier is more robust for LC types which have various

scattering mechanisms Comparison of PolInSAR classification and ALOS (optical) LC product

PolInSAR classification is good on Forest, Urban, Bare and Water classification

ALOS (optical) LC product is good on Low vegetation (Paddy, Crop and Grass) classification

Conclusions PALSAR PolInSAR data has high capability for LC monitoring Quad-PolInSAR classification is more accurate than dual-PolInSAR and

quad/dual-PolSAR The SVM is better than the Wishart classifier on classification accuracy

Future Works Improvement of classification algorithm

Other classification methods Other feature parameters Speckle filtering, terrain correction

Extension of the test area Application for monitoring disaster, forest or agriculture PolInSAR data of ALOS-2/PALSAR-2:

higher resolution, smaller and stable orbit distance...

Thank you for your attention…

Mt. Tsukuba

Tsukuba city

WaterPaddyCropGrassForestUrbanBare

Forest/Urban misclassification issue Scattering mechanism of urban area varies depending on

their orientation angle Pi-SAR L-band data ~ 3m resolution

Simulated PALSAR’s resolution

“Non-orthogonal” urban is confusing with forest

range

azimuth

Aerial photo ©Yahoo! JapanForest

UrbanUrban

?

?Urban

Orthogonal Non-orthogonal

HH-VVHV

HH+VV

Detail – Paddy (2) Back-scattering in paddy area changes significantly from April to May

#1 02/04/2007Before flooding

#2 18/05/2007After flooding

Optical (AVNIR-2) 15/05/2007HH-VV

HVHH+VV

Soil surface

Water surface

Reference data Truth land-cover data made by interpreting:

Land-use 100m mesh data (FY 2006) ©GSI, Japan Optical images (ALOS/AVNIR-2)

Coordinate conversion (projected on the slant-range coordinate) No. of samples: 8200 (on the slant-range of the PLR mode data)

→half of them used as training data, the others used for evaluation

Mode Pointing Path/Row Observation date Cloud cover (auto)

OBS 0.0° 67/2870-2880 18/05/200716/08/2007

30-40%0-10%

LatLon

AzimuthRange

Coordinateconversion

Polarimetric Interferometry (PolInSAR) Combination of PolSAR + InSAR Contains many feature parameters: amplitudes & coherences

References Formulation and the model (Cloude & Papathanassiou, 1998) Decomposition (Papathanassiou & Cloude, 2003; Neumann et al.,

2005) Application (Forest biomass, urban detection, agriculture…)

Land-cover monitoring (e.g. Shimoni et al., 2009)

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Interferometric coherence for LC types

Water < Bare soilForest < Urban

HH VV

HV

HH+VV HH–VV

Amplitude for LC types

Water ≈ Bare soilForest ≈ Urbanconfusing

HH VV

HV

HH+VV HH–VV