Imaging Radar Polarimetric Rotation Domain Interpretation : Theory and Applications

Polarimetric rotation domain interpretation is an innovation in radar image processing and understanding. Orientation rotation is a basic operator well known in the classic polarimetry theory, and significant advancement has been made in recent years. This book presents new and advanced concepts, theories, and methodologies in radar polarimetry and bridges the gaps between target scattering diversity, polarimetric radar data, and their practical applications. It provides a comprehensive summarization and investigation of polarimetric rotation domain features and demonstrates novel applications of polarimetric radar target detection, classification, target structure recognition, and urban damage mapping.

FEATURES

• Focuses on basic concepts, key techniques, and various applications of the polarimetric rotation domain interpretation paradigm for the first time in book form
• Explains, represents, and utilizes the radar target scattering diversity effect
• Identifies new methods for target polarimetric scattering mechanism understanding
• Provides a comprehensive investigation of polarimetric roll-invariant features
• Includes novel application developments for imaging radar target detection, structure recognition, and damage mapping

This book is written for researchers and professionals in radar polarimetry, radar imaging, microwave remote sensing, environmental studies, and other related fields. Senior undergraduate and postgraduate students, as well as teachers in the same fields, will benefit from the advancements highlighted in this book.

Si-Wei Chen earned a PhD (Hons.) in environmental studies at Tohoku University, Sendai, Japan, in 2012. He is a Professor at the National University of Defense Technology. He has published more than 130 journal and conference articles and coauthored three monographs and holds 28 patents. His research interests include radar polarimetry, polarimetric radar imaging, environmental study, and machine learning. Dr. Chen was a recipient of the Excellent Young Scholar from the National Natural Science Foundation of China, a recipient of the IEEE Geoscience and Remote Sensing Society (GRSS) Early Career Award, the Natural Science Foundation of Hunan Province for Distinguished Young Scholars, the Young Talent of Hunan Province, China. He won the First Prize of Invention and Innovation Award from China Association of Inventions in 2023, the Second Prize of the National Teaching Achievement Award in 2023, the Second Prize of the National Award for Progress in Science and Technology in 2021, and the First Prize of the Natural Science Award from Hunan Province of China in 2018. He is an Associate Editor of the IEEE Geoscience and Remote Sensing Letters and an Editorial Board Member of the Journal of Remote Sensing, Journal of Electronics and Information Technology, Journal of Radars, Journal of Signal Processing, and Journal of National University of Defense Technology.