Radar Scattering and Imaging of Rough Surfaces: Modeling and Applications with MATLAB® (SAR Remote Sensing)

Description

Radar scattering and imaging of rough surfaces is an active interdisciplinary area of research with many practical applications in fields such as mineral and resource exploration, ocean and physical oceanography, military and national defense, planetary exploration, city planning and land use, environmental science, and many more. By focusing on the most advanced analytical and numerical modeling and describing both forward and inverse modeling, Radar Scattering and Imaging of Rough Surfaces: Modeling and Applications with MATLAB® connects the scattering process to imaging techniques by vivid examples through numerical and experimental demonstrations and provides computer codes and practical uses. This book is unique in its simultaneous treatment of radar scattering and imaging. Key Features Bridges physical modeling with simulation for resolving radar imaging problems (the first comprehensive work to do so) Provides excellent basic and advanced information for microwave remote-sensing professionals in various fields of science and engineering Covers most advanced analytical and numerical modeling for both backscattering and bistatic scattering Includes MATLAB® codes useful not only for academics but also for radar engineers and scientists to develop tools applicable in different areas of earth studies Covering both the theoretical and the practical, Radar Scattering and Imaging of Rough Surfaces: Modeling and Applications with MATLAB® is an invaluable resource for professionals and students using remote sensing to study and explain the Earth and its processes. University and research institutes, electrical and radar engineers, remote-sensing image users, application software developers, students, and academics alike will benefit from this book. The author, Kun-Shan Chen, is an internationally known and respected engineer and scientist and an expert in the field of electromagnetic modeling. Cover Half Title Series Page Title Page Copyright Page Dedication Dedication Contents Preface Acknowledgments Author 1. Introduction 1.1. Surface Scattering as a Random Process 1.2. Nature of Wave Scattering from Rough Surface 1.3. Radar Imaging Mechanisms and Computation 1.4. Progress of the Subject References 2. Statistical Description of Rough Surfaces 2.1. Types of Rough Surface 2.1.1. Quasi-Periodic and Random Rough Surfaces 2.1.2. Isotropic and Anisotropic Surfaces 2.2. Statistics of Randomly Rough Surface 2.2.1. Fractal Approach 2.2.2. σ-ℓ Approach 2.3. Correlation Functions and Roughness Spectra 2.4. Multiscale Rough Surface 2.5. Uncertainties of Roughness Parameters References 3. Basics of Electromagnetic Wave 3.1. Maxwell's Equations 3.2. Constitute Relations 3.3. Wave Reflection and Transmission at a Plane Boundary 3.3.1. Laws of Reflection and Refraction 3.3.2. Reflection and Transmission in a Layered Medium 3.4. Radar Equations for Objects and Extensive Targets 3.4.1. Radar Cross-Section 3.4.2. Scattering Coefficient References 4. Analytical Modeling of Rough Surface Scattering 4.1. Huygens-Fresnel Principle 4.2. Electric Field Integral Equations (EFIE) and Magnetic Field Integral Equations (MFIE) 4.3. Solutions of EFIE and MFIE--Numerical and Analytical Approaches 4.3.1. Kirchhoff Approximation (KA) 4.3.2. Small Perturbation Method (SPM) 4.3.3. Small Slope Approximation (SSA) 4.3.4. Integral Equation Model (IEM) 4.4. Advanced Integral Equation Models 4.4.1. Single and Multiple Scattering 4.4.2. Multiple Scattering Contributions 4.4.3. Validation of AIEM Model 4.4.3.1 Comparison with Numerical Simulations 4.4.3.2 Comparison with Measurement Data 4.4.3.3 Comparison between POLARSCAT, NMM3D, SSA2, AIEM, and SPM2 4.5. Numerical Examples for Soil and Ocean Surfaces 4.5.1. Scattering Behaviors from Soil Surface 4.5.1.1 Radar Backscattering Behaviors 4.5.1.1.1 Surface Roughness Effects 4.5.1.1.2 Dielectric Constant Effects 4.5.1.2 Radar Bistatic Scattering Behaviors 4.5.1.2.1 Transition Rate Effects 4.5.1.2.2 Background Dielectric Constant Effects 4.5.2. Scattering Behaviors from Ocean Surface 4.5.2.1 Radar Backscattering Behaviors 4.5.2.2 Radar Bistatic Scattering Behaviors References Appendix 4A Appendix 4B Appendix 4C 5. Sensitivity Analysis of Radar Scattering of Rough Surface 5.1. Extended Fourier Amplitude Sensitivity Test (EFAST) 5.2. Entropy-Based Sensitivity Analysis (EBSA) 5.3. Monostatic vs. Bistatic Scattering Patterns 5.3.1. Monostatic Scattering Patterns 5.3.1.1. Distribution Response of the Backscattering Coefficient 5.3.1.2. SA by Information Entropy 5.3.2. Bistatic Scattering Patterns 5.4. Dependences on Radar Parameters 5.4.1. Sensitivity to Incident Angle 5.4.2. Sensitivity to Polarization 5.4.3. Sensitivity to Multi-Angle 5.5. Dependences on Surface Parameters 5.5.1. Sensitivity to Soil Moisture 5.5.2. Sensitivity to RMS Height 5.5.3. Sensitivity to Correlation Length References 6. Geophysical Parameters Estimation 6.1. Bayesian Estimation 6.2. Cramer-Rao Bound 6.3. Least Square Estimation 6.4. Kalman Filter-Based Estimation References 7. Selected Model Applications to Remote Sensing 7.1. Surface Parameter Response to Radar Observations--A Quick Look 7.1.1. Comparison with POLARSCAT Data 7.1.1.1 For Surface 1 (S1) 7.1.1.2 For Surface 2 (S2) 7.1.1.3 For Surface 3 (S3) 7.1.2. Comparison with EMSL Data 7.1.3. Comparison with SMOSREX06 Data 7.2. Surface Parameter Retrieval 7.2.1. Data Input-Output and Training Samples 7.2.2. Retrieval Results Using Backscattering Coefficients 7.2.3. Retrieval Using Bistatic Scattering Coefficients 7.2.4. Comparison with Image-Based Surface Parameter Estimation from Polarimetric SAR Image Data 7.3. Direction Estimation of Incident Source: A Data Analytic Example References 8. Radar Imaging Techniques 8.1. Stochastic Wave Equations 8.2. Time-Reversal Imaging 8.3. Synthetic Aperture Imaging 8.3.1. Signal Model 8.3.2. SAR Path Trajectory 8.3.3. Antenna Beam Tracking 8.3.4. Simulation Examples 8.4. Mutual Coherence Function 8.5. Bistatic SAR Imaging 8.5.1. Bistatic SAR Scattering Property 8.5.2. Bistatic Imaging Geometry and Signal Model 8.5.3. Bistatic Range History 8.5.4. Examples References Appendix 8A Appendix 8B 9. Computational Electromagnetic Imaging of Rough Surfaces 9.1. Rough Surfaces Fabrication by 3D Printing 9.2. Experimental Measurements and Calibration 9.3. Data Acquisition and Image Formation 9.4. Image Statistics and Quality References 10. Advanced Topic: A Moon-Based Imaging of Earth's Surface 10.1. Radar Moon-Earth Geometry 10.1.1. Time and Space Coordinates 10.1.2. Transformations from the ECI to ECR 10.2. Spatiotemporal Coverage 10.2.1. Geometric Parameters 10.2.2. Effective Range 10.2.3. Moon-based SAR's Spatial Coverage 10.2.4. Temporal Variations in the Spatial Coverage 10.2.5. Numerical Illustration of Spatiotemporal Coverage 10.2.5.1 Hourly Variations 10.2.5.2 Global Accumulated Visible Time within Different Periods 10.3. Propagation through Ionospheric Layers 10.3.1. Phase Error due to Temporal-Spatial Varying Background Ionosphere 10.3.2. Slant Range in the Context of Background Ionospheric Effects 10.3.3. SAR Signal in the Context of Background Ionospheric Effects 10.4. Image Distortions by Dispersive Effects 10.4.1. Ionospheric Effects on Range Imaging 10.4.1.1 Range shift 10.4.1.2 Range Defocusing 10.4.2. Ionospheric Effects on Azimuth Imaging 10.4.2.1 Azimuth Shift 10.4.2.2 Azimuth Defocusing 10.5. Image Simulations and Error Analysis 10.5.1. Imaging of Point Targets 10.5.2. Imaging of Extended Target References Index