Principles of Synthetic Aperture Radar Imaging: A System Simulation Approach demonstrates the use of image simulation for SAR. It covers the various applications of SAR (including feature extraction, target classification, and change detection), provides a complete understanding of SAR principles, and illustrates the complete chain of a SAR operation.
The book places special emphasis on a ground-based SAR, but also explains space and air-borne systems. It contains chapters on signal speckle, radar-signal models, sensor-trajectory models, SAR-image focusing, platform-motion compensation, and microwave-scattering from random media.
While discussing SAR image focusing and motion compensation, it presents processing algorithms and applications that feature extraction, target classification, and change detection. It also provides samples of simulation on various scenarios, and includes simulation flowcharts and results that are detailed throughout the book.
Introducing SAR imaging from a systems point of view, the author:
Principles of Synthetic Aperture Radar Imaging: A System Simulation Approach is dedicated to the use, study, and development of SAR systems. The book focuses on image formation or focusing, treats platform motion and image focusing, and is suitable for students, radar engineers, and microwave remote sensing researchers.
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Kun-Shan Chen received a PhD degree in electrical engineering from the University of Texas at Arlington in 1990. From 1992 to 2014, he was with the faculty of National Central University, Taiwan. He joined the Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, in 2014, and has served the Department of Electrical Engineering, The University of Texas at Arlington, USA, as a research professor since 2014. He has authored or coauthored over 120 journal papers, contributed seven book chapters, is a coauthor of one book, and a fellow of The Institute of Electrical and Electronics Engineers (IEEE).Review :
"This book provides readers with a comprehensive and complete description of synthetic aperture radar principle. Its unique feature of full-blown SAR image simulations and modeling, including sensor and target location, targets geometric and radiometric scattering characteristics, and clutters from system and environment, distinguishes this book from other SAR processing books. Insightful and state of arts information on SAR trajectory, SAR focusing and motion compensation are clearly detailed. For the first time, satellite SAR systems, such as RadarSAT-2, TerraSAR-X and ALOS/PALSAR and their imaging configurations, are integrated into the simulation and modeling. One of the highlights of this book rests at image simulations of hard targets, such as B757-200, B747-400, A321 and MD80 for various aspect angles. These simulations make target identification possible as illustrated using real TerraSAR-X SAR images. This book can be used both as a reference book for SAR researchers and as a textbook for graduate students if exercises can be supplemented."
―Jon-Sen Lee, Naval Research Laboratory (Retired), Washington DC, USA
"The book is based on professor Kun-Shan Chen’s vast experience, both theoretical and practical, over the last three decades. It covers topics in SAR systems which are relevant for senior undergraduate students, graduate students, engineers and researchers. The algorithms for motion compensation and image formation; and demonstration of applications of these are particularly useful."
―Professor Academician Dr. Hean-Teik Chuah, FIEEE, President of Universiti Tunku Abdul Rahman, Malaysia
"No book of this scope and depth comes about casually, and this book is no exception. The book is sharply focused and seems remarkably free of typos, which is testimony of the care and attention given by the author and the editors. Principles of Synthetic Aperture Radar Imaging: A Systems Simulation Approach has been faithful to its title and as such is an excellent source for understanding what is required of a first rate SAR sensor, for users, designers, and developers alike."
―Photogrammetric Engineering & Remote Sensing, January 2017
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