Polarimetric Radar Imaging: From Basics to Applications

TLDR: In this article, the authors used a two-dimensional time-frequency approach to evaluate the effect of speckle properties in SAR images and showed that the effect on the spatial correlation of the specckle sparseness of SAR images can be influenced by the number of multilook-processed SAR images.

Abstract: Overview of Polarimetric Radar Imaging Brief History of Polarimetric Radar Imaging SAR Image Formation: Summary Airborne and Space-Borne PolSAR Systems Description of the Remaining Chapters Electromagnetic Vector Wave and Polarization Descriptors Monochromatic Electromagnetic Plane Wave Polarization Ellipse Jones Vector Stokes Vector Wave Covariance Matrix Electromagnetic Vector Scattering Operators Polarimetric Back Scattering Sinclair S Matrix Scattering Target Vectors k and Omega Polarimetric Coherency T and Covariance C Matrices Polarimetric Mueller M and Kennaugh K Matrices Change of Polarimetric Basis Target Polarimetric Characterization PolSAR Speckle Statistics Fundamental Property of Speckle in SAR Images Speckle Statistics for Multilook-Processed SAR Images Texture Model and K Distribution Effect of Speckle Spatial Correlation Polarimetric and Interferometric SAR Speckle Statistics Phase Difference Distributions of Single-Look and Multilook PolSAR Data Multilook Product Distribution Joint Distribution of Multilook Si2 and Sj2 Multilook Intensity and Amplitude Ratio Distributions Verifications of Multilook PDFs K Distribution for Multilook Polarimetric Data Summary Appendices PolSAR Speckle Filtering Introduction to Speckle Filtering of SAR Imagery Filtering of Single Polarization SAR Data Review of Multipolarization Speckle Filtering Algorithms PolSAR Speckle Filtering Scattering Model-Based PolSAR Speckle Filter Introduction to the Polarimetric Target Decomposition Concept Introduction Dichotomy of the Kennaugh Matrix K Eigenvector-Based Decompositions Model-Based Decompositions Coherent Decompositions The H/A/a Polarimetric Decomposition Theorem Introduction Pure Target Case Probabilistic Model for Random Media Scattering Roll Invariance Property Polarimetric Scattering a Parameter Polarimetric Scattering Entropy (H) Polarimetric Scattering Anisotropy (A) Three-Dimensional H/A/a Classification Space New Eigenvalue-Based Parameters Speckle Filtering Effects on H/A/a PolSAR Terrain and Land-Use Classification Introduction Maximum Likelihood Classifier Based on Complex Gaussian Distribution Complex Wishart Classifier for Multilook PolSAR Data Characteristics of Wishart Distance Measure Supervised Classification Using Wishart Distance Measure Unsupervised Classification Based on Scattering Mechanisms and Wishart Classifier Scattering Model-Based Unsupervised Classification Quantitative Comparison of Classification Capability: Fully PolSAR versus Dual- and Single-Polarization SAR Pol-InSAR Forest Mapping and Classification Introduction Pol-InSAR Scattering Descriptors Forest Mapping and Forest Classification Appendix Selected PolSAR Applications Polarimetric Signature Analysis of Manmade Structures Polarization Orientation Angle Estimation and Applications Ocean Surface Remote Sensing with PolSAR Ionosphere Faraday Rotation Estimation PolSAR Interferometry for Forest Height Estimation Nonstationary Natural Media Analysis from PolSAR Data Using a Two-Dimensional Time-Frequency Approach Appendix A: Eigen Characteristics of Hermitian Matrix Appendix B: PolSARpro Software: The Polariemtric SAR Data Processing and Educational Toolbox Index