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E-raamat: Radar Scattering and Imaging of Rough Surfaces: Modeling and Applications with MATLAB(R)

(Chinese Academy of Sciences, Beijing, China)
  • Formaat: 339 pages
  • Sari: SAR Remote Sensing
  • Ilmumisaeg: 19-Nov-2020
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781351011563
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Radar Scattering and Imaging of Rough Surfaces: Modeling and Applications with MATLAB(R)Suurem pilt
  • Formaat: 339 pages
  • Sari: SAR Remote Sensing
  • Ilmumisaeg: 19-Nov-2020
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781351011563
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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.

    Preface xi
    Acknowledgments xiii
    Author xv
    Chapter 1 Introduction
    		1(22)
    1.1 Surface Scattering as a Random Process
    		1(3)
    1.2 Nature of Wave Scattering from Rough Surface
    		4(1)
    1.3 Radar Imaging Mechanisms and Computation
    		5(8)
    1.4 Progress of the Subject
    		13(5)
    References
    		18(5)
    Chapter 2 Statistical Description of Rough Surfaces
    		23(16)
    2.1 Types of Rough Surface
    		23(2)
    2.1.1 Quasi-Periodic and Random Rough Surfaces
    		23(1)
    2.1.2 Isotropic and Anisotropic Surfaces
    		24(1)
    2.2 Statistics of Randomly Rough Surface
    		25(5)
    2.2.1 Fractal Approach
    		25(3)
    2.2.2 j-fc Approach
    		28(2)
    2.3 Correlation Functions and Roughness Spectra
    		30(3)
    2.4 Multiscale Rough Surface
    		33(3)
    2.5 Uncertainties of Roughness Parameters
    		36(2)
    References
    		38(1)
    Chapter 3 Basics of Electromagnetic Wave
    		39(20)
    3.1 Maxwell's Equations
    		39(1)
    3.2 Constitute Relations
    		40(2)
    3.3 Wave Reflection and Transmission at a Plane Boundary
    		42(9)
    3.3.1 Laws of Reflection and Refraction
    		42(3)
    3.3.2 Reflection and Transmission in a Layered Medium
    		45(6)
    3.4 Radar Equations for Objects and Extensive Targets
    		51(6)
    3.4.1 Radar Cross-Section
    		51(4)
    3.4.2 Scattering Coefficient
    		55(2)
    References
    		57(2)
    Chapter 4 Analytical Modeling of Rough Surface Scattering
    		59(66)
    4.1 Huygens-Fresnel Principle
    		59(2)
    4.2 Electric Field Integral Equations (EFIE) and Magnetic Field Integral Equations (MFIE)
    		61(3)
    4.3 Solutions of EFIE and MFIE--Numerical and Analytical Approaches
    		64(5)
    4.3.1 Kirchhoff Approximation (KA)
    		66(1)
    4.3.2 Small Perturbation Method (SPM)
    		67(1)
    4.3.3 Small Slope Approximation (SSA)
    		68(1)
    4.3.4 Integral Equation Model (IEM)
    		69(1)
    4.4 Advanced Integral Equation Models
    		69(17)
    4.4.1 Single and Multiple Scattering
    		70(7)
    4.4.2 Multiple Scattering Contributions
    		77(3)
    4.4.3 Validation of AIEM Model
    		80(1)
    4.4.3.1 Comparison with Numerical Simulations
    		80(1)
    4.4.3.2 Comparison with Measurement Data
    		81(5)
    4.4.3.3 Comparison between POLARSCAT, NMM3D, SSA2, AIEM, and SPM2
    		86(1)
    4.5 Numerical Examples for Soil and Ocean Surfaces
    		86(29)
    4.5.1 Scattering Behaviors from Soil Surface
    		86(1)
    4.5.1.1 Radar Backscattering Behaviors
    		86(3)
    4.5.1.2 Radar Bistatic Scattering Behaviors
    		89(7)
    4.5.2 Scattering Behaviors from Ocean Surface
    		96(1)
    4.5.2.1 Radar Backscattering Behaviors
    		96(15)
    4.5.2.2 Radar Bistatic Scattering Behaviors
    		111(4)
    References
    		115(3)
    Appendix 4A
    		118(2)
    Appendix 4B
    		120(1)
    Appendix 4C
    		121(4)
    Chapter 5 Sensitivity Analysis of Radar Scattering of Rough Surface
    		125(22)
    5.1 Extended Fourier Amplitude Sensitivity Test (EFAST)
    		125(1)
    5.2 Entropy-Based Sensitivity Analysis (EBSA)
    		126(2)
    5.3 Monostatic vs. Bistatic Scattering Patterns
    		128(3)
    5.3.1 Monostatic Scattering Patterns
    		128(1)
    5.3.1.1 Distribution Response of the Backscattering Coefficient
    		128(1)
    5.3.1.2 SA by Information Entropy
    		128(1)
    5.3.2 Bistatic Scattering Patterns
    		129(2)
    5.4 Dependences on Radar Parameters
    		131(3)
    5.4.1 Sensitivity to Incident Angle
    		131(2)
    5.4.2 Sensitivity to Polarization
    		133(1)
    5.4.3 Sensitivity to Multi-Angle
    		133(1)
    5.5 Dependences on Surface Parameters
    		134(9)
    5.5.1 Sensitivity to Soil Moisture
    		136(2)
    5.5.2 Sensitivity to RMS Height
    		138(2)
    5.5.3 Sensitivity to Correlation Length
    		140(3)
    References
    		143(4)
    Chapter 6 Geophysical Parameters Estimation
    		147(12)
    6.1 Bayesian Estimation
    		147(4)
    6.2 Cramer-Rao Bound
    		151(1)
    6.3 Least Square Estimation
    		152(2)
    6.4 Kalman Filter-Based Estimation
    		154(4)
    References
    		158(1)
    Chapter 7 Selected Model Applications to Remote Sensing
    		159(22)
    7.1 Surface Parameter Response to Radar Observations--A Quick Look
    		160(5)
    7.1.1 Comparison with POLARSCAT Data
    		160(1)
    7.1.1.1 For Surface 1 (SI)
    		160(1)
    7.1.1.2 For Surface 2 (S2)
    		160(3)
    7.1.1.3 For Surface 3 (S3)
    		163(1)
    7.1.2 Comparison with EMSL Data
    		164(1)
    7.1.3 Comparison with SMOSREX06 Data
    		165(1)
    7.2 Surface Parameter Retrieval
    		165(10)
    7.2.1 Data Input-Output and Training Samples
    		165(3)
    7.2.2 Retrieval Results Using Backscattering Coefficients
    		168(1)
    7.2.3 Retrieval Using Bistatic Scattering Coefficients
    		168(5)
    7.2.4 Comparison with Image-Based Surface Parameter Estimation from Polarimetric SAR Image Data
    		173(2)
    7.3 Direction Estimation of Incident Source: A Data Analytic Example
    		175(3)
    References
    		178(3)
    Chapter 8 Radar Imaging Techniques
    		181(56)
    8.1 Stochastic Wave Equations
    		181(10)
    8.2 Time-Reversal Imaging
    		191(7)
    8.3 Synthetic Aperture Imaging
    		198(13)
    8.3.1 Signal Model
    		198(3)
    8.3.2 SAR Path Trajectory
    		201(3)
    8.3.3 Antenna Beam Tracking
    		204(1)
    8.3.4 Simulation Examples
    		204(7)
    8.4 Mutual Coherence Function
    		211(4)
    8.5 Bistatic SAR Imaging
    		215(15)
    8.5.1 Bistatic SAR Scattering Property
    		216(2)
    8.5.2 Bistatic Imaging Geometry and Signal Model
    		218(5)
    8.5.3 Bistatic Range History
    		223(2)
    8.5.4 Examples
    		225(5)
    References
    		230(3)
    Appendix 8A
    		233(2)
    Appendix 8B
    		235(2)
    Chapter 9 Computational Electromagnetic Imaging of Rough Surfaces
    		237(26)
    9.1 Rough Surfaces Fabrication by 3D Printing
    		237(2)
    9.2 Experimental Measurements and Calibration
    		239(4)
    9.3 Data Acquisition and Image Formation
    		243(5)
    9.4 Image Statistics and Quality
    		248(12)
    References
    		260(3)
    Chapter 10 Advanced Topic: A Moon-Based Imaging of Earth's Surface
    		263(54)
    10.1 Radar Moon-Earth Geometry
    		263(5)
    10.1.1 Time and Space Coordinates
    		264(2)
    10.1.2 Transformations from the ECI to ECR
    		266(2)
    10.2 Spatiotemporal Coverage
    		268(15)
    10.2.1 Geometric parameters
    		268(4)
    10.2.2 Effective Range
    		272(3)
    10.2.3 Moon-Based SAR's Spatial Coverage
    		275(3)
    10.2.4 Temporal Variations in the Spatial Coverage
    		278(1)
    10.2.5 Numerical Illustration of Spatiotemporal Coverage
    		279(1)
    10.2.5.1 Hourly Variations
    		279(1)
    10.2.5.2 Global Accumulated Visible Time within Different periods
    		280(3)
    10.3 Propagation through Ionospheric layers
    		283(9)
    10.3.1 Phase Error due to Temporal-Spatial Varying Background Ionosphere
    		284(3)
    10.3.2 Slant Range in the Context of Background Ionospheric Effects
    		287(2)
    10.3.3 SAR Signal in the Context of Background Ionospheric Effects
    		289(3)
    10.4 Image Distortions by Dispersive Effects
    		292(17)
    10.4.1 Ionospheric Effects on Range Imaging
    		292(1)
    10.4.1.1 Range shift
    		293(4)
    10.4.1.2 Range Defocusing
    		297(7)
    10.4.2 Ionospheric Effects on Azimuth Imaging
    		304(1)
    10.4.2.1 Azimuth Shift
    		304(3)
    10.4.2.2 Azimuth Defocusing
    		307(2)
    10.5 Image Simulations and Error Analysis
    		309(4)
    10.5.1 Imaging of Point Targets
    		310(1)
    10.5.2 Imaging of Extended Target
    		311(2)
    References
    		313(4)
    Index 317
    Kun-Shan Chen earned a PhD in electrical engineering at the University of Texas at Arlington in 1990. From 1992 to 2014, he was a professor at the National Central University, Taiwan. From 2014 to 2019, he was with the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China. Since 2019, he has been a professor at Guilin University of Technology, where his research interests include microwave remote sensing theory, modeling, system, and measurement, and intelligent signal processing and data analytics for radar.

    He has authored or co-authored over 160 refereed journal papers, contributed 10 book chapters, co-authored (with A. K. Fung) Microwave Scattering and Emission Models for Users (Artech House, 2010), authored Principles of Synthetic Aperture Radar: A System Simulation Approach (CRC Press, 2015), and co-edited (with X. Li, H. Guo, X. Yang) Advances in SAR Remote Sensing of Ocean (CRC Press, 2018).

    His academic activities include being a guest editor for a special issue on Remote Sensing for Major Disaster Prevention, Monitoring and Assessment (2007) in IEEE Transactions on Geoscience and Remote Sensing, a guest editor for the special issue on Remote Sensing for Natural Disaster (2012) in Proceedings of the IEEE, IEEE GRSS Adcom member (20102014), a founding chair of the GRSS Taipei Chapter, an associate editor of the IEEE Transactions on Geoscience and Remote Sensing since 2000, founding deputy editor-in-chief of IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (20082010). He served as guest editor of the special issue of Data Restoration and Denoising of Remote Sensing Data and special issues of Radar Imaging Theory, Techniques, and Applications, both for Remote Sensing, and was co-chair of the Technical Committee for IGARSS 2016 and IGARSS 2017. He served as a member of the editorial board of the Proceedings of the IEEE (20142019) and has been a member of the editorial board of the IEEE Access since 2020. He is a Fellow of IEEE.
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