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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?
1
11
11
1
22
1
HV
VVHH
VVHH
S
SS
SS
k
2
22
22
2
22
1
HV
VVHH
VVHH
S
SS
SS
k
T
T
T
*2112
*2222
*1111
kkT
kkT
kkT
22*12
12116 TT
TTT T
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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1
HV
VVHH
VVHH
S
SS
SS
k
2
22
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2
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1
HV
VVHH
VVHH
S
SS
SS
kT
T
T
*2112
*2222
*1111
kkT
kkT
kkT
22*12
12116 TT
TTT T
Master
Slave
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