Polarimetric Scattering and SAR Information Retrieval

Ya-Qiu Jin, Fudan University, ChinaProfessor Jin is Chair Professor and Director of the Key Lab of Wave Scattering and Remote Sensing Information, at Fudan University, Shanghai, China. He is an IEEE Fellow, a Fellow of the Electromagnetics Academy (USA) and CIE as well as being Chair of the IEEE Fellow Evaluation Committee (GRSS), a Member of IEEE GRSS AdCom, and Associate Editor of IEEE Transactions on Geoscience and Remote Sensing.Feng Xu, Intelligent Automation, Inc, USADr. Xu holds the post of Research Scientist at Intelligent Automation, Inc, Rockville, USA. He took his PhD at Fudan University in Shanghai, China and was a postdoctoral researcher at NOAA/NESDIS, USA, from 2008-2009.

• Preface xi1 Basics of Polarimetric Scattering 11.1 Polarized Electromagnetic Wave 11.1.1 Jones Vector and Scattering Matrix 11.1.2 Stokes Vector and Mueller Matrix 41.2 Volumetric Scattering 91.2.1 Small Particle under Rayleigh–Gans Approximation 91.2.2 Slim Cylinder 111.3 Surface Scattering 131.3.1 Plane Surface 131.3.2 Rough Surface 141.3.3 Kirchhoff Approximation 161.3.4 Small-Perturbation Approximation 191.3.5 Two-Scale Approximation 211.3.6 Integral Equation Method 221.3.7 Tilted Surface or Oriented Object 25References 262 Vector Radiative Transfer 292.1 Radiative Transfer Equation 292.1.1 Specific Intensity and Stokes Vector 292.1.2 Thermal Emission and Brightness Temperature 322.1.3 Vector Radiative Transfer Equation 332.2 Components in Radiative Transfer Equation 352.2.1 Scattering, Absorption, and Extinction Coefficients 352.2.2 Extinction Matrix 362.2.3 Phase Matrix 392.3 Mueller Matrix Solution 402.3.1 First-Order Mueller Matrix Solution 402.3.2 Modeling of Vegetation Canopy over Rough Surface 432.3.3 Numerical Examples of Modeling of Vegetation Canopy 472.4 Polarization Indices and Entropy 522.4.1 Eigen-Analysis of Mueller Matrix 522.4.2 Relationship between Eigenvalues, Entropy, and Polarization Indices 532.4.3 Demonstration with AirSAR Imagery 552.5 Statistics of Stokes Parameters 592.5.1 Multi-Look Covariance Matrix and Complex Wishart Distribution 592.5.2 PDFs of the Four Stokes Parameters 602.5.3 Comparison with AirSAR Image Data 67Appendix 2A: Phase Matrix of Non-Spherical Particles 72References 763 Imaging Simulation of Polarimetric SAR: Mapping and Projection Algorithm 793.1 Fundamentals of SAR Imaging 793.1.1 Ranging and Pulse Compression 793.1.2 Synthetic Aperture and Azimuth Focusing 823.1.3 SAR Imaging Algorithm 853.2 Mapping and Projection Algorithm 903.2.1 Mapping and Projection 913.2.2 Mapping and Projection Algorithm for Fast Computation 953.2.3 Scattering Models for Terrain Objects 1013.2.4 Speckle Model and Raw Data Generation 1053.3 Platform for SAR Simulation 1083.3.1 Simulation of Individual Terrain Objects 1083.3.2 Simulation of Comprehensive Terrain Scene 1123.3.3 Extensions 119References 1214 Bistatic SAR: Simulation, Processing, and Interpretation 1234.1 Bistatic Mapping and Projection Algorithm (BI-MPA) 1244.1.1 Configurations of BISAR 1244.1.2 Three-Dimensional Projection and Mapping 1254.1.3 Multiple Scattering Terms 1304.2 Scattering Models and Signal Model 1304.2.1 Models of Terrain Objects 1304.2.2 Raw Signal Model for BISAR 1314.3 Simulated BISAR Images 1364.4 Polarimetric Characteristics of BISAR Image 1414.5 Unified Bistatic Polarization Bases 1464.6 Raw Signal Processing of Stripmap BISAR 1504.6.1 Approximate Form of the Point Target Response 1504.6.2 Validity Condition and an Iterative Solution 1554.6.3 Extension of Range Doppler Method 1574.6.4 Simulation and Discussion 159References 1645 Radar Polarimetry and Deorientation Theory 1675.1 Radar Polarimetry and Target Decomposition 1675.1.1 Polarization Transformation 1675.1.2 Radar Polarimetry 1735.1.3 Target Decomposition 1805.2 Deorientation Theory 1845.2.1 Deorientation 1845.2.2 Efficacy of Deorientation 1925.3 Terrain Surface Classification 1985.3.1 Terrain Scattering Modeling and Classification Spectrum 1985.3.2 Application to SIR-C Data 2015.3.3 Orientation Analysis 206Appendix 5A: Matrix Transformations under Various Conventions 2075a.1 Transformation under Wave Coordinates (FSA) 2095a.2 Transformation under Antenna Coordinates (BSA) 2115a.3 Interconversion between Wave Coordinates (FSA) and Antenna Coordinates (BSA) 213References 2136 Inversions from Polarimetric SAR Images 2156.1 Inversion of Digital Elevation Mapping 2166.1.1 The Shift of Orientation Angle 2166.1.2 Range and Azimuth Angles from Euler Angle Transformation 2186.1.3 The Azimuth Angle of Every Pixel in a SAR Image 2206.2 An Example of Algorithm Implementation 2216.3 Inversion of Bridge Height 2256.3.1 Geometric Rays Projection for Analysis of Bridge Scattering 2256.3.2 SAR Image Simulation of a Bridge Object 2266.3.3 Inversion of Naruto Bridge Height using Classification Parameters 2276.3.4 Inversion of the Height of Eastern Sea Bridge using ALOS SAR Data 231References 2337 Automatic Reconstruction of Building Objects from Multi-Aspect SAR Images 2357.1 Detection and Extraction of Object Image 2377.1.1 Features of a Simple Building Object in a SAR Image with Meter Resolution 2377.1.2 CFAR Edge Detection and Ridge Filter Thinning 2387.1.3 Building Image Extraction via Hough Transform 2427.1.4 Classification of Building Images 2467.2 Building Reconstruction from a Multi-Aspect Image 2477.2.1 Probabilistic Description of Building Image 2477.2.2 Multi-Aspect Coherence of Building Images 2507.2.3 Multi-Aspect Co-Registration 2557.3 Automatic Multi-Aspect Reconstruction (AMAR) 2577.4 Results and Discussion 2607.4.1 Analysis of Reconstruction Results 2607.4.2 Discussion 2647.5 Calibration and Validation of Multi-Aspect SAR Data 2657.5.1 Method 2657.5.2 Experiments 268References 2738 Faraday Rotation on Polarimetric SAR Image at UHF/VHF Bands 2758.1 Faraday Rotation Effect on Terrain Surface Classification 2768.1.1 Faraday Rotation in Plasma Media 2768.1.2 Mueller Matrix with Faraday Rotation 2788.2 Recovering the Mueller Matrix with Ambiguity Error p/ 2 2838.3 Method to Eliminate the p/2 Ambiguity Error 287References 2909 Change Detection from Multi-Temporal SAR Images 2919.1 The 2EM-MRF Algorithm 2929.1.1 The EM Algorithm 2929.1.2 Two-Thresholds EM Algorithm 2939.1.3 Spatial–Textual Classification based on MRF 2959.2 The 2EM-MRF for Change Detection in an Urban Area 2989.3 Change Detection after the 2008 Wenchuan Earthquake 301References 30810 Temporal Mueller Matrix for Polarimetric Scattering 31110.1 Radiative Transfer in Inhomogeneous Random Scattering Media 31210.2 Time-Dependent Mueller Matrix for Inhomogeneous Random Media 31710.3 Polarimetric Bistatic and Backscattering Pulse Responses 32110.4 Pulse Echoes from Lunar Regolith Layer 32810.4.1 Mueller Matrix Solution for Seven Scattering Mechanisms 32910.4.2 Numerical Simulation of Pulse Echoes 33310.4.3 Pulse Echo Images of Lunar Layered Media 33610.5 Monitoring Debris and Landslides 33910.5.1 Modeling Debris Flows and Landslides 34010.5.2 Echo Simulation of Nadir Looking Radar 342Appendix 10A: Some Mathematics Needed in Derivation of Temporal Mueller Matrix 346References 34811 Fast Computation of Composite Scattering from an Electrically Large Target over a Randomly Rough Surface 35111.1 Bidirectional Analytic Ray Tracing 35211.1.1 Bidirectional Tracing 35211.1.2 Analytic Tracing 35611.1.3 Rough Facets 36011.2 Numerical Results 361References 37412 Reconstruction of a 3D Complex Target using Downward-Looking Step-Frequency Radar 37512.1 Principle of 3D Reconstruction 37612.1.1 Principle of Imaging based on Point Scattering 37612.1.2 Imaging Algorithm using 3D Fast Fourier Transform 37912.1.3 Resolution and Sampling Criteria 38212.2 Scattering Simulation and 3D Reconstruction 38212.2.1 Model of Square Frustum (Case I) 38312.2.2 Model of Tank-Like Target (Case II) 38612.2.3 Tank-Like Model over Rough Surface (Case III) 388References 392Index 395