Evaluation of supervised land-cover classification by PALSAR polarimetric interferometry<br />Masato Ohki and Masanobu Shi...
Outline<br />Background<br />Polarimetric interferometry (PolInSAR)<br />PALSAR PolInSAR data<br />Methods and data<br />R...
Background<br />
PALSAR polarimetry data<br />PLR (quad-PoLaRimetric mode) Specification:<br />Off-nadir angle: ≤ 26.1°<br />Ground resolut...
PALSAR Polarimetric Interferometry (PolInSAR)<br />Issue: single satellite-> repeat-pass  interferometry<br />Various spat...
The quake hit Tsukuba Space Center<br />What can we do for disasterprevention/mitigation?<br />3.11 Earthquake<br />
Overview of this study<br /> Feasibility study on land-cover (LC) monitoring by PALSAR<br />7 classes supervised LC classi...
Methods and Data<br />
Test data<br />PALSAR data used in this study<br />                  #1 (PLR)             #2 (PLR)                        ...
Truth LC data<br />Truth land-cover data was made by interpreting:<br />Land-use 100m mesh data (2006) ©GSI, Japan<br />Op...
Class definition<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />Reference data(10...
Processing Procedure<br />1. Pre-processing(imaging, pol. calibration and interferometry)<br />Processor: SIGMA-SAR (by Dr...
Classifier(1) – Wishart Classifier<br />Maximum likelihood approach assuming that the scattering matrix follows a complex ...
Classifier(2) – Support Vector Machine (SVM)<br />Margin maximization approach discriminating a class from other classesin...
Results and Discussion<br />
Classification result (method: SVM)<br />Quad-PolInSAR       Dual-PolInSAR        Quad-PolSAR             Dual-PolSAR<br /...
Comparison with optical image<br />                            Quad-PolInSAR        Optical image(ALOS/AVNIR-2)<br />Wate...
Classification result (method: Wishart)<br />Quad-PolInSAR       Dual-PolInSAR        Quad-PolSAR             Dual-PolSAR<...
Comparison of SVM and Wishart<br /> Quad-PolInSAR(SVM)      Quad-PolInSAR(Wishart)<br />Water<br />Paddy<br />Crop<br /...
Evaluation result (confusion matrices)<br /> Quad-PolInSAR (method: SVM)          Quad-PolInSAR (method: Wishart)<br /> Du...
Evaluation result – summary<br />Method: SVM<br />Method: Wishart<br />>    >    ><br />>    >    ><br />*Calculation time...
Detail – Urban area<br />urban area = high coherence-> PolInSAR effectiveness for discriminating urban<br />Water<br />P...
Detail – Paddy<br />paddy area = lower coherence-> PolInSAR effectiveness for detecting paddy areas<br />Water<br />Padd...
Comparison of classification methods<br />Some LC types (esp. urban)can have various scattering mechanism<br />Linear clas...
ALOS Land-cover product (by the optical sensor)<br />Available at http://www.eorc.jaxa.jp/ALOS/lulc/lulc_jindex.htm (free)...
Comparison with ALOS LC product<br />     PolInSAR (this study)    Optical (ALOS LC product)<br />Water<br />Paddy<br />...
Comparison with ALOS LC product<br />Advantage of PolInSAR classification:<br />Precise detection ofForest, Urban, Bare an...
Comparison with ALOS LC product <br />Advantage of optical classification<br />Precise detection of low vegetation (Paddy,...
Summary of results<br />Comparison of datasets<br />Accuracy: Quad-PolInSAR > Dual-PolInSAR> Quad-Pol > Dual-Pol<br />Inte...
Conclusions<br />PALSAR PolInSAR data has high capability for LC monitoring<br />Quad-PolInSAR classification is more accu...
Thank you for your attention…<br />Mt. Tsukuba<br />Tsukuba city<br />Water<br />Paddy<br />Crop<br />Grass<br />Fore...
Forest/Urban misclassification issue<br />Scattering mechanism of urban area varies depending on their orientation angle<b...
Detail – Paddy (2)<br />Back-scattering in paddy area changes significantly from April to May<br />#1  02/04/2007<br />Bef...
Reference data<br />Truth land-cover data made by interpreting:<br />Land-use 100m mesh data (FY 2006) ©GSI, Japan<br />Op...
Polarimetric Interferometry (PolInSAR)<br />Combination of PolSAR + InSAR<br />Contains many feature parameters: amplitude...
Interferometric coherence for LC types<br />HH<br />VV<br />Water < Bare soil<br />Forest < Urban<br /><br />HV<br />HH+V...
Amplitude for LC types<br />HH<br />VV<br />Water ≈ Bare soil<br />Forest ≈ Urban<br />confusing<br />HV<br />HH+VV<br />...
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201107IGARSS_OHKI.pptx

  1. 1. Evaluation of supervised land-cover classification by PALSAR polarimetric interferometry<br />Masato Ohki and Masanobu Shimada<br />Earth Observation Research Center, Japan Aerospace Exploration Agency<br />
  2. 2. Outline<br />Background<br />Polarimetric interferometry (PolInSAR)<br />PALSAR PolInSAR data<br />Methods and data<br />Result: Land-cover classification by PALSAR PolInSAR<br />Discussion<br />Advantage of PolInSAR for LC classification<br />Comparison between classification methods<br />Comparison with optical sensor data<br />Conclusion and Future work<br />
  3. 3. Background<br />
  4. 4. PALSAR polarimetry data<br />PLR (quad-PoLaRimetric mode) Specification:<br />Off-nadir angle: ≤ 26.1°<br />Ground resolution: ~25m (at 21.5°)<br />Swath width: ~35km (at 21.5°)<br />Capable of interferometry(minimum temporal distance: 46 days)<br />PLR data coverage (2006-2011)<br />PALSAR<br />ALOS<br />ALOS-2<br />PALSAR-2<br />
  5. 5. PALSAR Polarimetric Interferometry (PolInSAR)<br />Issue: single satellite-> repeat-pass interferometry<br />Various spatial distance (0.0~2.5km)<br />Long temporal distance (≥46 days)<br />-> Application?<br />Master<br />PolInSARCoherency matrix<br />repeat pass<br />Slave<br />rm<br />rs<br />
  6. 6. The quake hit Tsukuba Space Center<br />What can we do for disasterprevention/mitigation?<br />3.11 Earthquake<br />
  7. 7. Overview of this study<br /> Feasibility study on land-cover (LC) monitoring by PALSAR<br />7 classes supervised LC classification by PALSAR PolInSAR data<br />Accuracy evaluation<br />Comparison between four cases of datasets:<br />(1) Quad-PolInSAR<br />(2) Dual-PolInSAR<br />(3) Quad-PolSAR<br />(4) Dual-PolSAR<br />Comparison between classification methods:<br />Wishart<br />SVM<br />Comparison with other LC product<br />ALOS LC product (optical)<br />
  8. 8. Methods and Data<br />
  9. 9. Test data<br />PALSAR data used in this study<br /> #1 (PLR) #2 (PLR) Optical (AVNIR-2)<br />Tsukuba city (36.05˚N,140.10˚E)<br />HH-VVHVHH+VV(Pauli)<br />NARITA Int’l Airport(35.77˚N,140.39˚E)<br />
  10. 10. Truth LC data<br />Truth land-cover data was made by interpreting:<br />Land-use 100m mesh data (2006) ©GSI, Japan<br />Optical images (ALOS/AVNIR-2) <br />Lat<br />Az<br />Training datafor classification(4100 samples)<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />Lon<br />Rg<br />Truth datafor evaluation(4100 samples)<br />100m mesh land-use, 2006©GSI, Japan (11 classes)<br />AVNIR-2 image(15 MAY 2007)<br /> Truth data(105 polygons, 8200 samples)<br />
  11. 11. Class definition<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />Reference data(105 polygons, 8200 samples)<br />#2 Paddy<br />#4 Grass<br />#3 Crop<br />#7 Bare<br />Ground photographs (Tsukuba city, 09 JUN 2009)<br />
  12. 12. Processing Procedure<br />1. Pre-processing(imaging, pol. calibration and interferometry)<br />Processor: SIGMA-SAR (by Dr. Shimada)<br />2. Classification<br />Compared two classificationmethods: Wishart classifier and SVM<br />Processor: developed in this study<br />3. Post-processing(ortho-rectification and geo-coding)<br />Processor: SIGMA-SAR (by Dr. Shimada)<br />Resolution of the classification map: 60m<br />PALSARL1.0(master)<br />PALSAR L1.0(Slave)<br />Generate SLC<br />Pol. CalibrationCo-registration<br />Slope correction (option)<br />Pol. filtering (option)<br />Trainingdataset<br />Classification<br />(Wishart or SVM)<br />DEM<br />Ortho-rectification(geo-coding)<br />Final classification map<br />
  13. 13. Classifier(1) – Wishart Classifier<br />Maximum likelihood approach assuming that the scattering matrix follows a complex Wishart distribution function (Lee et al., 1994, 1999)<br />The pixel is assigned to the class minimizing the distance measure between the pixel and the training class<br />Scattering matrix for the Wishart classifier<br />(master data)<br />(master data)<br />
  14. 14. Classifier(2) – Support Vector Machine (SVM)<br />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)<br />Feature parameters for the SVM<br />*The Cloude-Pottier decomposition (Cloude & Pottier, 1996; Pottier 1998)<br />
  15. 15. Results and Discussion<br />
  16. 16. Classification result (method: SVM)<br />Quad-PolInSAR Dual-PolInSAR Quad-PolSAR Dual-PolSAR<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />
  17. 17. Comparison with optical image<br /> Quad-PolInSAR Optical image(ALOS/AVNIR-2)<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />
  18. 18. Classification result (method: Wishart)<br />Quad-PolInSAR Dual-PolInSAR Quad-PolSAR Dual-PolSAR<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />
  19. 19. Comparison of SVM and Wishart<br /> Quad-PolInSAR(SVM) Quad-PolInSAR(Wishart)<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />
  20. 20. Evaluation result (confusion matrices)<br /> Quad-PolInSAR (method: SVM) Quad-PolInSAR (method: Wishart)<br /> Dual-PolInSAR (method: SVM) Quad-PolSAR (method: SVM)<br />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(%)<br />
  21. 21. Evaluation result – summary<br />Method: SVM<br />Method: Wishart<br />> > ><br />> > ><br />*Calculation time: CPU elapsed time for training and classifying<br />
  22. 22. Detail – Urban area<br />urban area = high coherence-> PolInSAR effectiveness for discriminating urban<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />Urban area<br />Urban area?<br /> Quad-PolInSAR (SVM) Quad-PolSAR (SVM)<br />Urban area<br /> Optical<br /> Coherence(HH-VVHVHH+VV)Amplitude<br />
  23. 23. Detail – Paddy<br />paddy area = lower coherence-> PolInSAR effectiveness for detecting paddy areas<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />Paddy areaoverestimated<br /> Quad-PolInSAR (SVM) Quad-PolSAR (SVM)<br />Paddy<br />Paddy<br /> Coherence(HH-VVHVHH+VV)Amplitude<br /> Optical<br />
  24. 24. Comparison of classification methods<br />Some LC types (esp. urban)can have various scattering mechanism<br />Linear classifier (e.g. Wishart)<br />Assuming a single scattering mechanism for each class<br />Non-linear or non-parametric classifier (e.g. SVM) <br />More robust for LC types which have various scattering mechanisms<br />Urban areamisclassifiedas Forest<br />Crop fieldsmisclassifiedas Grass<br />Grassmisclassifiedas Bare<br /> Quad-PolInSAR (SVM) Quad-PolInSAR (Wishart) Optical<br />
  25. 25. ALOS Land-cover product (by the optical sensor)<br />Available at http://www.eorc.jaxa.jp/ALOS/lulc/lulc_jindex.htm (free)<br />Current version: ver. 11.02 (released on Feb 2011)<br />Classification method: decision tree of multi-seasonal optical sensor images<br />Coverage: Japan area<br />No. of classes: 10<br />Resolution: 30m<br />Accuracy: 87%(evaluation result)<br />ALOS LC product (optical)<br />
  26. 26. Comparison with ALOS LC product<br /> PolInSAR (this study) Optical (ALOS LC product)<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />
  27. 27. Comparison with ALOS LC product<br />Advantage of PolInSAR classification:<br />Precise detection ofForest, Urban, Bare and Water<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />Small urban areamisclassified as Forest<br /> PolInSAR (this study) Optical (ALOS LC) AVNIR-2 image<br />Bare groundsmisclassifiedas Water<br />
  28. 28. Comparison with ALOS LC product <br />Advantage of optical classification<br />Precise detection of low vegetation (Paddy, Crop and Grass)<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />Grass areamisclassifiedas Crop<br /> PolInSAR (this study) Optical (ALOS LC) AVNIR-2 image<br />Paddymisclassifiedas Crop<br />
  29. 29. Summary of results<br />Comparison of datasets<br />Accuracy: Quad-PolInSAR > Dual-PolInSAR> Quad-Pol > Dual-Pol<br />Interferometric coherence plays important roles for discriminating LC types which have confusing scattering mechanisms<br />Comparison of classification methods:<br />Accuracy: SVM > Wishart<br />Computation Speed: Wishart > SVM<br />Non-linear classifier is more robust for LC types which have various scattering mechanisms<br />Comparison of PolInSAR classification and ALOS (optical) LC product<br />PolInSAR classification is good on Forest, Urban, Bare and Water classification<br />ALOS (optical) LC product is good on Low vegetation (Paddy, Crop and Grass) classification<br />
  30. 30. Conclusions<br />PALSAR PolInSAR data has high capability for LC monitoring<br />Quad-PolInSAR classification is more accurate than dual-PolInSAR and quad/dual-PolSAR<br />The SVM is better than the Wishart classifier on classification accuracy<br />Future Works<br />Improvement of classification algorithm<br />Other classification methods<br />Other feature parameters<br />Speckle filtering, terrain correction<br />Extension of the test area<br />Application for monitoring disaster, forest or agriculture<br />PolInSAR data of ALOS-2/PALSAR-2: higher resolution, smaller and stable orbit distance... <br />
  31. 31. Thank you for your attention…<br />Mt. Tsukuba<br />Tsukuba city<br />Water<br />Paddy<br />Crop<br />Grass<br />Forest<br />Urban<br />Bare<br />
  32. 32.
  33. 33. Forest/Urban misclassification issue<br />Scattering mechanism of urban area varies depending on their orientation angle<br />Pi-SAR L-band data ~ 3m resolution<br />Simulated PALSAR’s resolution<br />“Non-orthogonal” urban is confusing with forest<br />Aerial photo ©Yahoo! Japan<br />Forest<br />Urban<br />Urban<br />Orthogonal<br />Non-orthogonal<br />range<br />azimuth<br />?<br />HH-VVHVHH+VV<br />Urban<br />?<br />
  34. 34. Detail – Paddy (2)<br />Back-scattering in paddy area changes significantly from April to May<br />#1 02/04/2007<br />Before flooding<br />#2 18/05/2007<br />After flooding<br />Optical (AVNIR-2) 15/05/2007<br />HH-VVHVHH+VV<br />Water surface<br />Soil surface<br />
  35. 35. Reference data<br />Truth land-cover data made by interpreting:<br />Land-use 100m mesh data (FY 2006) ©GSI, Japan<br />Optical images (ALOS/AVNIR-2) <br />Coordinate conversion (projected on the slant-range coordinate)<br />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<br />Lat<br />Azimuth<br />Lon<br />Range<br />Coordinateconversion <br />
  36. 36. Polarimetric Interferometry (PolInSAR)<br />Combination of PolSAR + InSAR<br />Contains many feature parameters: amplitudes & coherences<br />References<br />Formulation and the model (Cloude & Papathanassiou, 1998)<br />Decomposition (Papathanassiou & Cloude, 2003; Neumann et al., 2005)<br />Application (Forest biomass, urban detection, agriculture…)<br />Land-cover monitoring(e.g. Shimoni et al., 2009)<br />Master<br />Slave<br />
  37. 37. Interferometric coherence for LC types<br />HH<br />VV<br />Water < Bare soil<br />Forest < Urban<br /><br />HV<br />HH+VV<br />HH–VV<br />
  38. 38. Amplitude for LC types<br />HH<br />VV<br />Water ≈ Bare soil<br />Forest ≈ Urban<br />confusing<br />HV<br />HH+VV<br />HH–VV<br />

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