Remote Sensing with Polarimetric Radar

HAROLD MOTT, PhD, is Professor Emeritus of Electrical Engineering at the University of Alabama. Dr. Mott has carried out polarimetric-radar research for NASA and NRL, and has consulted extensively with the Army Missile Command on radar environmental modeling. He is the author of Polarization in Antennas and Radar and Antennas for Radar and Communications: A Polarimetric Approach (both published by Wiley).

• Preface xiiAcknowledgments xv1. Electromagnetic Waves 11.1. The Time-Invariant Maxwell Equations 21.2. The Electromagnetic Traveling Wave 31.3. Power Density 61.4. The Polarization Ellipse 71.5. Polarization Vector and Polarization Ratio 111.6. Circular Wave Components 111.7. Change of Polarization Basis 121.8. Ellipse Characteristics in Terms of P and Q 141.9. Coherency and Stokes Vectors 151.10. The Poincaré Sphere 17References 19Problems 192. Antennas 212.1. Elements of the Antenna System 212.2. The Vector Potentials 222.3. Solutions for the Vector Potentials 242.4. Far-Zone Fields 252.5. Radiation Pattern 282.6. Gain and Directivity 302.7. The Receiving Antenna 342.8. Transmission Between Antennas 412.9. Antenna Arrays 412.10. Effective Length of an Antenna 472.11. Reception of Completely Polarized Waves 482.12. Gain, Effective Area, and Radiation Resistance 512.13. Maximum Received Power 522.14. Polarization Efficiency 522.15. The Modified Friis Transmission Equation 542.16. Alignment of Antennas 54References 57Problems 573. Coherently Scattering Targets 593.1. Radar Targets 593.2. The Jones Matrix 613.3. The Sinclair Matrix 623.4. Matrices With Relative Phase 643.5. FSA–BSA Conventions 653.6. Relationship Between Jones and Sinclair Matrices 653.7. Scattering with Circular Wave Components 663.8. Backscattering 673.9. Polarization Ratio of the Scattered Wave 683.10. Change of Polarization Basis: The Scattering Matrix 683.11. Polarizations for Maximum and Minimum Power 703.12. The Polarization Fork 773.13. Nonaligned Coordinate Systems 813.14. Determination of Scattering Parameters 82References 88Problems 894. An Introduction to Radar 914.1. Pulse Radar 924.2. CW Radar 984.3. Directional Properties of Radar Measurements 984.4. Resolution 994.5. Imaging Radar 1044.6. The Traditional Radar Equation 1054.7. The Polarimetric Radar Equation 1074.8. A Polarimetric Radar 1084.9. Noise 110References 117Problems 1175. Synthetic Aperture Radar 1195.1. Creating a Terrain Map 1195.2. Range Resolution 1245.3. Azimuth Resolution 1255.4. Geometric Factors 1325.5. Polarimetric SAR 1335.6. SAR Errors 1335.7. Height Measurement 1365.8. Polarimetric Interferometry 1415.9. Phase Unwrapping 142References 147Problems 1476. Partially Polarized Waves 1496.1. Representation of the Fields 1506.2. Representation of Partially Polarized Waves 1546.3. Reception of Partially Polarized Waves 164References 166Problems 1667. Scattering by Depolarizing Targets 1697.1. Targets 1707.2. Averaging the Sinclair Matrix 1737.3. The Kronecker-Product Matrices 1747.4. Matrices for a Depolarizing Target: Coherent Measurement 1777.5. Incoherently Measured Target Matrices 1787.6. Matrix Properties and Relationships 1867.7. Modified Matrices 1897.8. Names 1917.9. Additional Target Information 1917.10. Target Covariance and Coherency Matrices 1927.11. A Scattering Matrix with Circular Components 1967.12. The Graves Power Density Matrix 1977.13. Measurement Considerations 1997.14. Degree of Polarization and Polarimetric Entropy 2007.15. Variance of Power 2017.16. Summary of Power Equations and Matrix Relationships 202References 204Problems 2048. Optimal Polarizations for Radar 2078.1. Antenna Selection Criteria 2078.2. Lagrange Multipliers 208A. Coherently Scattering Targets 2098.3. Maximum Power 2098.4. Power Contrast: Backscattering 211B. Depolarizing Targets 2118.5. Iterative Procedure for Maximizing Power Contrast 2128.6. The Backscattering Covariance Matrix 2158.7. The Bistatic Covariance Matrix 2168.8. Maximizing Power Contrast by Matrix Decomposition 2178.9. Optimization with the Graves Matrix 218References 222Problems 2239. Classification of Targets 225A. Classification Concepts 2259.1. Representation and Classification of Targets 2269.2. Bayes Decision Rule 2289.3. The Neyman–Pearson Decision Rule 2319.4. Bayes Error Bounds 2329.5. Estimation of Parameters from Data 2329.6. Nonparametric Classification 236B. Classification by Matrix Decomposition 2429.7. Coherent Decomposition 2439.8. Decomposition of Power-Type Matrices 245C. Removal of Unpolarized Scattering 2499.9. Decomposition of the D Matrix 2499.10. Polarized Clutter 2559.11. A Similar Decomposition 2559.12. Polarimetric Similarity Classification 256References 256Problems 257Appendix A. Fading and Speckle 259Reference 261Appendix B. Probability and Random Processes 263B.1. Probability 263B.2. Random Variables 273B.3. Random Vectors 279B.4. Probability Density Functions in Remote Sensing 287B.5. Random Processes 288References 294Appendix C. The Kennaugh Matrix 295Appendix D. Bayes Error Bounds 299References 301Index 303

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