1. Evaluation of supervised land-cover classification by PALSAR polarimetric interferometry Masato Ohki and Masanobu Shimada Earth Observation Research Center, Japan Aerospace Exploration Agency

2. 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

3. Background

4. 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

5. PALSAR Polarimetric Interferometry (PolInSAR) Issue: single satellite-> repeat-pass interferometry Various spatial distance (0.0~2.5km) Long temporal distance (≥46 days) -> Application? Master PolInSARCoherency matrix repeat pass Slave rm rs

6. The quake hit Tsukuba Space Center What can we do for disasterprevention/mitigation? 3.11 Earthquake

7. 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)

8. Methods and Data

9. Test data PALSAR data used in this study #1 (PLR) #2 (PLR) Optical (AVNIR-2) Tsukuba city (36.05˚N,140.10˚E) HH-VVHVHH+VV(Pauli) NARITA Int’l Airport(35.77˚N,140.39˚E)

10. Truth LC data Truth land-cover data was made by interpreting: Land-use 100m mesh data (2006) ©GSI, Japan Optical images (ALOS/AVNIR-2) Lat Az Training datafor classification(4100 samples) Water Paddy Crop Grass Forest Urban Bare Lon Rg Truth datafor evaluation(4100 samples) 100m mesh land-use, 2006©GSI, Japan (11 classes) AVNIR-2 image(15 MAY 2007) Truth data(105 polygons, 8200 samples)

11. Class definition Water Paddy Crop Grass Forest Urban Bare Reference data(105 polygons, 8200 samples) #2 Paddy #4 Grass #3 Crop #7 Bare Ground photographs (Tsukuba city, 09 JUN 2009)

12. Processing Procedure 1. Pre-processing(imaging, pol. calibration and interferometry) Processor: SIGMA-SAR (by Dr. Shimada) 2. Classification Compared two classificationmethods: 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 PALSARL1.0(master) PALSAR L1.0(Slave) Generate SLC Pol. CalibrationCo-registration Slope correction (option) Pol. filtering (option) Trainingdataset Classification (Wishart or SVM) DEM Ortho-rectification(geo-coding) Final classification map

13. 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 (master data) (master data)

14. Classifier(2) – Support Vector Machine (SVM) Margin maximization approach discriminating a class from other classesin 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 SVM *The Cloude-Pottier decomposition (Cloude & Pottier, 1996; Pottier 1998)

15. Results and Discussion

16. Classification result (method: SVM) Quad-PolInSAR Dual-PolInSAR Quad-PolSAR Dual-PolSAR Water Paddy Crop Grass Forest Urban Bare

17. Comparison with optical image Quad-PolInSAR Optical image(ALOS/AVNIR-2) Water Paddy Crop Grass Forest Urban Bare

18. Classification result (method: Wishart) Quad-PolInSAR Dual-PolInSAR Quad-PolSAR Dual-PolSAR Water Paddy Crop Grass Forest Urban Bare

19. Comparison of SVM and Wishart Quad-PolInSAR(SVM) Quad-PolInSAR(Wishart) Water Paddy Crop Grass Forest Urban Bare

20. Evaluation result (confusion matrices) 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(%)

21. Evaluation result – summary Method: SVM Method: Wishart > > > > > > *Calculation time: CPU elapsed time for training and classifying

22. Detail – Urban area urban area = high coherence-> PolInSAR effectiveness for discriminating urban Water Paddy Crop Grass Forest Urban Bare Urban area Urban area? Quad-PolInSAR (SVM) Quad-PolSAR (SVM) Urban area Optical Coherence(HH-VVHVHH+VV)Amplitude

23. Detail – Paddy paddy area = lower coherence-> PolInSAR effectiveness for detecting paddy areas Water Paddy Crop Grass Forest Urban Bare Paddy areaoverestimated Quad-PolInSAR (SVM) Quad-PolSAR (SVM) Paddy Paddy Coherence(HH-VVHVHH+VV)Amplitude Optical

24. 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 Urban areamisclassifiedas Forest Crop fieldsmisclassifiedas Grass Grassmisclassifiedas Bare Quad-PolInSAR (SVM) Quad-PolInSAR (Wishart) Optical

25. 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)

26. Comparison with ALOS LC product PolInSAR (this study) Optical (ALOS LC product) Water Paddy Crop Grass Forest Urban Bare

27. Comparison with ALOS LC product Advantage of PolInSAR classification: Precise detection ofForest, Urban, Bare and Water Water Paddy Crop Grass Forest Urban Bare Small urban areamisclassified as Forest PolInSAR (this study) Optical (ALOS LC) AVNIR-2 image Bare groundsmisclassifiedas Water

28. Comparison with ALOS LC product Advantage of optical classification Precise detection of low vegetation (Paddy, Crop and Grass) Water Paddy Crop Grass Forest Urban Bare Grass areamisclassifiedas Crop PolInSAR (this study) Optical (ALOS LC) AVNIR-2 image Paddymisclassifiedas Crop

29. 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

30. 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...

31. Thank you for your attention… Mt. Tsukuba Tsukuba city Water Paddy Crop Grass Forest Urban Bare

33. 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 Aerial photo ©Yahoo! Japan Forest Urban Urban Orthogonal Non-orthogonal range azimuth ? HH-VVHVHH+VV Urban ?

34. Detail – Paddy (2) Back-scattering in paddy area changes significantly from April to May #1 02/04/2007 Before flooding #2 18/05/2007 After flooding Optical (AVNIR-2) 15/05/2007 HH-VVHVHH+VV Water surface Soil surface

35. 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 Lat Azimuth Lon Range Coordinateconversion

36. 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) Master Slave

37. Interferometric coherence for LC types HH VV Water < Bare soil Forest < Urban  HV HH+VV HH–VV

38. Amplitude for LC types HH VV Water ≈ Bare soil Forest ≈ Urban confusing HV HH+VV HH–VV