Polarimetric radar imaging : from basics to applications

The recent launches of three fully polarimetric synthetic aperture radar (PolSAR) satellites have shown that polarimetric radar imaging can provide abundant data on the Earth's environment, such as biomass and forest height estimation, snow cover mapping, glacier monitoring, and damage assessment. Written by two of the most recognized leaders in this field, Polarimetric Radar Imaging: From Basics to Applications presents polarimetric radar imaging and processing techniques and shows how to develop remote sensing applications using PolSAR imaging radar.

The book provides a substantial and balanced introduction to the basic theory and advanced concepts of polarimetric scattering mechanisms, speckle statistics and speckle filtering, polarimetric information analysis and extraction techniques, and applications typical to radar polarimetric remote sensing. It explains the importance of wave polarization theory and the speckle phenomenon in the information retrieval problem of microwave imaging and inverse scattering. The authors demonstrate how to devise intelligent information extraction algorithms for remote sensing applications. They also describe more advanced polarimetric analysis techniques for polarimetric target decompositions, polarization orientation effects, polarimetric scattering modeling, speckle filtering, terrain and forest classification, manmade target analysis, and PolSAR interferometry.

With sample PolSAR data sets and software available for download, this self-contained, hands-on book encourages you to analyze space-borne and airborne PolSAR and polarimetric interferometric SAR (Pol-InSAR) data and then develop applications using this data.

Author Notes

Lee, Jong-Sen; Pottier, Eric

Foreword	p. xix
Acknowledgements	p. xxi
Authors	p. xxiii
Chapter 1 Overview of Polarimetric Radar Imaging	p. 1
1.1 Brief History of Polarimetric Radar Imaging	p. 1
1.1.1 Introduction	p. 1
1.1.2 Development of Imaging Radar	p. 2
1.1.3 Development of Polarimetric Radar Imaging	p. 2
1.1.4 Education of Polarimetric Radar Imaging	p. 4
1.2 SAR Image Formation: Summary	p. 5
1.2.1 Introduction	p. 5
1.2.2 SAR Geometric Configuration	p. 6
1.2.3 SAR Spatial Resolution	p. 8
1.2.4 SAR Image Processing	p. 9
1.2.5 SAR Complex Image	p. 10
1.3 Airborne and Space-Borne Polarimetric SAR Systems	p. 13
1.3.1 Introduction	p. 13
1.3.2 Airborne Polarimetric SAR Systems	p. 14
1.3.2.1 AIRSAR (NASA/JPL)	p. 14
1.3.2.2 CONVAIR-580 C/X-SAR (CCRS/EC)	p. 16
1.3.2.3 EMISAR (DCRS)	p. 16
1.3.2.4 E-SAR (DLR)	p. 16
1.3.2.5 PI-SAR (JAXA-NICT)	p. 17
1.3.2.6 RAMSES (ONERA-DEMR)	p. 17
1.3.2.7 SETHI (ONERA-DEMR)	p. 18
1.3.3 Space-Borne Polarimetric SAR Systems	p. 19
1.3.3.1 SIR-C/X SAR (NASA/DARA/ASI)	p. 19
1.3.3.2 ENVISAT-ASAR (ESA)	p. 19
1.3.3.3 ALOS-PALSAR (JAXA/JAROS)	p. 20
1.3.3.4 TerraSAR-X (BMBF/DLR/Astrium GmbH)	p. 21
1.3.3.5 RADARSAT-2 (CSA/MDA)	p. 22
1.4 Description of the Chapters	p. 22
References	p. 28
Chapter 2 Electromagnetic Vector Wave and Polarization Descriptors	p. 31
2.1 Monochromatic Electromagnetic Plane Wave	p. 31
2.1.1 Equation of Propagation	p. 31
2.1.2 Monochromatic Plane Wave Solution	p. 32
2.2 Polarization Ellipse	p. 34
2.3 Jones Vector	p. 37
2.3.1 Definition	p. 37
2.3.2 Special Unitary Group SU(2)	p. 38
2.3.3 Orthogonal Polarization States and Polarization Basis	p. 40
2.3.4 Change of Polarimetric Basis	p. 41
2.4 Stokes Vector	p. 43
2.4.1 Real Representation of a Plane Wave Vector	p. 43
2.4.2 Special Unitary Group O(3)	p. 46
2.5 Wave Covariance Matrix	p. 47
2.5.1 Wave Degree of Polarization	p. 47
2.5.2 Wave Anisotropy and Wave Entropy	p. 48
2.5.3 Partially Polarized Wave Dichotomy Theorem	p. 49
References	p. 51
Chapter 3 Electromagnetic Vector Scattering Operators	p. 53
3.1 Polarimetric Backscattering Sinclair S Matrix	p. 53
3.1.1 Radar Equation	p. 53
3.1.2 Scattering Matrix	p. 55
3.1.3 Scattering Coordinate Frameworks	p. 61
3.2 Scattering Target Vectors ¿ and ¿	p. 63
3.2.1 Introduction	p. 63
3.2.2 Bistatic Scattering Case	p. 63
3.2.3 Monostatic Backscattering Case	p. 65
3.3 Polarimetric Coherency T and Covariance C Matrices	p. 66
3.3.1 Introduction	p. 66
3.3.2 Bistatic Scattering Case	p. 66
3.3.3 Monostatic Backscattering Case	p. 67
3.3.4 Scattering Symmetry Properties	p. 69
3.3.5 Eigenvector/Eigenvalues Decomposition	p. 72
3.4 Polarimetric Mueller M and Kennaugh K Matrices	p. 73
3.4.1 Introduction	p. 73
3.4.2 Monostatic Backscattering Case	p. 74
3.4.3 Bistatic Scattering Case	p. 77
3.5 Change of Polarimetric Basis	p. 80
3.5.1 Monostatic Backscattering Matrix S	p. 80
3.5.2 Polarimetric Coherency T Matrix	p. 83
3.5.3 Polarimetric Covariance C Matrix	p. 84
3.5.4 Polarimetric Kennaugh K Matrix	p. 84
3.6 Target Polarimetric Characterization	p. 85
3.6.1 Introduction	p. 85
3.6.2 Target Characteristic Polarization States	p. 87
3.6.2.1 Characteristic Target Polarization States in the Copolar Configuration	p. 88
3.6.2.2 Characteristic Polarization States in the Cross-Polar Configuration	p. 88
3.6.3 Diagonalization of the Sinclair S Matrix	p. 89
3.6.4 Canonical Scattering Mechanism	p. 92
3.6.4.1 Sphere, Flat Plate, Trihedral	p. 92
3.6.4.2 Horizontal Dipole	p. 93
3.6.4.3 Oriented Dipole	p. 94
3.6.4.4 Dihedral	p. 95
3.6.4.5 Right Helix	p. 96
3.6.4.6 Left Helix	p. 97
References	p. 98
Chapter 4 Polarimetric SAR Speckle Statistics	p. 101
4.1 Fundamental Property of Speckle in SAR Images	p. 101
4.1.1 Speckle Formation	p. 101
4.1.2 Rayleigh Speckle Model	p. 102
4.2 Speckle Statistics for Multilook-Processed SAR Images	p. 105
4.3 Texture Model and K-Distribution	p. 108
4.3.1 Normalized N-Look Intensity K-Distribution	p. 108
4.3.2 Normalized N-Look Amplitude K-Distribution	p. 109
4.4 Effect of Speckle Spatial Correlation	p. 110
4.4.1 Equivalent Number of Looks	p. 111
4.5 Polarimetric and Interferometric SAR Speckle Statistics	p. 112
4.5.1 Complex Gaussian and Complex Wishart Distribution	p. 112
4.5.2 Monte Carlo Simulation of Polarimetric SAR Data	p. 114
4.5.3 Verification of the Simulation Procedure	p. 115
4.5.4 Complex Correlation Coefficient	p. 115
4.6 Phase Difference Distributions of Single- and Multilook Polarimetric SAR Data	p. 116
4.6.1 Alternative Form of Phase Difference Distribution	p. 120
4.7 Multilook Product Distribution	p. 120
4.8 Joint Distribution of Multilook \|Si\|2 and \|Sj\|2	p. 121
4.9 Multilook Intensity and Amplitude Ratio Distributions	p. 122
4.10 Verification of Multilook PDFs	p. 125
4.11 K-Distribution for Multilook Polarimetric Data	p. 130
4.12 Summary	p. 135
Appendix 4.A p. 136
Appendix 4.B p. 138
Appendix 4.C p. 140
Appendix 4.D p. 140
References	p. 141
Chapter 5 Polarimetric SAR Speckle Filtering	p. 143
5.1 Introduction to Speckle Filtering of SAR Imagery	p. 143
5.1.1 Speckle Noise Model	p. 144
5.1.1.1 Speckle Noise Model for Polarimetric SAR Data	p. 146
5.2 Filtering of Single Polarization SAR Data	p. 147
5.2.1 Minimum Mean Square Filter	p. 149
5.2.1.1 Deficiencies of the Minimum Mean Square Error (MMSE) Filter	p. 150
5.2.2 Speckle Filtering with Edge-Aligned Window: Refined Lee Filter	p. 150
5.3 Review of Multipolarization Speckle Filtering Algorithms	p. 152
5.3.1 Polarimetric Whitening Filter	p. 153
5.3.2 Extension of PWF to Multilook Polarimetric Data	p. 156
5.3.3 Optimal Weighting Filter	p. 157
5.3.4 Vector Speckle Filtering	p. 158
5.4 Polarimetric SAR Speckle Filtering	p. 160
5.4.1 Principle of PolSAR Speckle Filtering	p. 160
5.4.2 Refined Lee PolSAR Speckle Filter	p. 161
5.4.3 Apply Region Growing Technique to PolSAR Speckle Filtering	p. 165
5.5 Scattering Model-Based PolSAR Speckle Filter	p. 166
5.5.1 Demonstration and Evaluation	p. 169
5.5.2 Speckle Reduction	p. 170
5.5.3 Preservation of Dominant Scattering Mechanism	p. 172
5.5.4 Preservation of Point Target Signatures	p. 174
References	p. 175
Chapter 6 Introduction to the Polarimetric Target Decomposition Concept	p. 179
6.1 Introduction	p. 179
6.2 Dichotomy of the Kennaugh Matrix K	p. 181
6.2.1 Phenomenological Huynen Decomposition	p. 181
6.2.2 Barnes-Holm Decomposition	p. 185
6.2.3 Yang Decomposition	p. 188
6.2.4 Interpretation of the Target Dichotomy Decomposition	p. 191
6.3 Eigenvector-Based Decompositions	p. 193
6.3.1 Cloude Decomposition	p. 195
6.3.2 Holm Decompositions	p. 195
6.3.3 van Zyl Decomposition	p. 198
6.4 Model-Based Decompositions	p. 200
6.4.1 Freeman-Durden Three-Component Decomposition	p. 200
6.4.2 Yamaguchi Four-Component Decomposition	p. 206
6.4.3 Freeman Two-Component Decomposition	p. 208
6.5 Coherent Decompositions	p. 213
6.5.1 Introduction	p. 213
6.5.2 Pauli Decomposition	p. 214
6.5.3 Krogager Decomposition	p. 215
6.5.4 Cameron Decomposition	p. 219
6.5.4.1 Scattering Matrix Coherent Decomposition	p. 219
6.5.4.2 Scattering Matrix Classification	p. 221
6.5.5 Polar Decomposition	p. 224
References	p. 225
Chapter 7 H/A/¿ Polarimetric Decomposition Theorem	p. 229
7.1 Introduction	p. 229
7.2 Pure Target Case	p. 229
7.3 Probabilistic Model for Random Media Scattering	p. 230
7.4 Roll Invariance Property	p. 232
7.5 Polarimetric Scattering ¿ Parameter	p. 234
7.6 Polarimetric Scattering Entropy (H)	p. 237
7.7 Polarimetric Scattering Anisotropy (A)	p. 237
7.8 Three-Dimensional H/A/¿ Classification Space	p. 239
7.9 New Eigenvalue-Based Parameters	p. 247
7.9.1 SERD and DERD Parameters	p. 247
7.9.2 Shannon Entropy	p. 249
7.9.3 Other Eigenvalue-Based Parameters	p. 251
7.9.3.1 Target Randomness Parameter	p. 251
7.9.3.2 Polarization Asymmetry and the Polarization Fraction Parameters	p. 252
7.9.3.3 Radar Vegetation Index and the Pedestal Height Parameters	p. 254
7.9.3.4 Alternative Entropy and Alpha Parameters Derivation	p. 255
7.10 Speckle Filtering Effects on H/A/¿	p. 257
7.10.1 Entropy (H) Parameter	p. 257
7.10.2 Anisotropy (A) Parameter	p. 259
7.10.3 Averaged Alpha Angle (¿) Parameter	p. 259
7.10.4 Estimation Bias on H/A/¿	p. 259
References	p. 262
Chapter 8 PolSAR Terrain and Land-Use Classification	p. 265
8.1 Introduction	p. 265
8.2 Maximum Likelihood Classifier Based on Complex Gaussian Distribution	p. 266
8.3 Complex Wishart Classifier for Multilook PolSAR Data	p. 267
8.4 Characteristics of Wishart Distance Measure	p. 268
8.5 Supervised Classification Using Wishart Distance Measure	p. 271
8.6 Unsupervised Classification Based on Scattering Mechanisms and Wishart Classifier	p. 274
8.6.1 Experiment Results	p. 276
8.6.2 Extension to H/¿/A and Wishart Classifier	p. 279
8.7 Scattering Model-Based Unsupervised Classification	p. 281
8.7.1 Experiment Results	p. 284
8.7.1.1 NASA/JPL AIRSAR San Francisco Image	p. 284
8.7.1.2 DLR E-SAR L-Band Oberpfaffenhofen Image	p. 286
8.7.2 Discussion	p. 288
8.8 Quantitative Comparison of Classification Capability: Fully Polarimetric SAR vs. Dual- and Single-Polarization SAR	p. 291
8.8.1 Supervised Classification Evaluation Based on Maximum Likelihood Classifier	p. 292
8.8.1.1 Classification Procedure	p. 292
8.8.1.2 Comparison of Crop Classification	p. 293
References	p. 299
Chapter 9 Pol-InSAR Forest Mapping and Classification	p. 301
9.1 Introduction	p. 301
9.2 Pol-InSAR Scattering Descriptors	p. 303
9.2.1 Polarimetric Interferometric Coherency T6 Matrix	p. 303
9.2.2 Complex Polarimetric Interferometric Coherence	p. 307
9.2.3 Polarimetric Interferometric Coherence Optimization	p. 308
9.2.4 Polarimetric Interferometric SAR Data Statistics	p. 313
9.3 Forest Mapping and Forest Classification	p. 314
9.3.1 Forested Area Segmentation	p. 314
9.3.2 Unsupervised Pol-InSAR Classification of the Volume Class	p. 314
9.3.3 Supervised Pol-InSAR Forest Classification	p. 318
Appendix 9.A p. 320
Derivation of Optimal Coherence Set Statistics	p. 320
References	p. 321
Chapter 10 Selected Polarimetric SAR Applications	p. 323
10.1 Polarimetric Signature Analysis of Man-Made Structures	p. 323
10.1.1 Slant Range of Multiple Bounce Scattering	p. 324
10.1.2 Polarimetric Signature of the Bridge during Construction	p. 325
10.1.3 Polarimetric Signature of the Bridge after Construction	p. 329
10.1.4 Conclusion	p. 332
10.2 Polarization Orientation Angle Estimation and Applications	p. 333
10.2.1 Radar Geometry of Polarization Orientation Angle	p. 333
10.2.2 Circular Polarization Covariance Matrix	p. 334
10.2.3 Circular Polarization Algorithm	p. 336
10.2.4 Discussion	p. 339
10.2.5 Orientation Angles Applications	p. 342
10.3 Ocean Surface Remote Sensing with Polarimetric SAR	p. 345
10.3.1 Cold Water Filament Detection	p. 345
10.3.2 Ocean Surface Slope Sensing	p. 346
10.3.3 Directional Wave Slope Spectra Measurement	p. 347
10.4 Ionosphere Faraday Rotation Estimation	p. 350
10.4.1 Faraday Rotation Estimation	p. 351
10.4.2 Faraday Rotation Angle Estimation from ALOS PALSAR Data	p. 353
10.5 Polarimetric SAR Interferometry for Forest Height Estimation	p. 354
10.5.1 Problems Associated with Coherence Estimation	p. 357
10.5.2 Adaptive Pol-InSAR Speckle Filtering Algorithm	p. 358
10.5.3 Demonstration Using E-SAR Glen Affric Pol-InSAR Data	p. 358
10.6 Nonstationary Natural Media Analysis from PolSAR Data Using a 2-D Time-Frequency Approach	p. 362
10.6.1 Introduction	p. 362
10.6.2 Principle of SAR Data Time-Frequency Analysis	p. 362
10.6.2.1 Time-Frequency Decomposition	p. 362
10.6.2.2 SAR Image Decomposition in Range and Azimuth	p. 363
10.6.2.3 Analysis in the Azimuth Direction	p. 364
10.6.2.4 Analysis in the Range Direction	p. 365
10.6.3 Discrete Time-Frequency Decomposition of Nonstationary Media PolSAR Response	p. 365
10.6.3.1 Anisotropic Polarimetric Behavior	p. 365
10.6.3.2 Decomposition in the Azimuth Direction	p. 366
10.6.3.3 Decomposition in the Range Direction	p. 368
10.6.4 Nonstationary Media Detection and Analysis	p. 369
References	p. 375
Appendix A Eigen Characteristics of Hermitian Matrix	p. 379
Reference	p. 384
Appendix B PolSARpro Software: The Polarimetric SAR Data Processing and Educational Toolbox	p. 385
B.1 Introduction	p. 385
B.2 Concepts and Principal Objectives	p. 385
B.3 Software Portability and Development Languages	p. 387
B.4 Outlook	p. 388
Index	p. 391

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