Radar Scattering and Imaging of Rough Surfaces (PDF)
Modeling and Applications with MATLAB®
(Sprache: Englisch)
Focusing on most advanced analytical and numerical modeling, this book connects scattering process to imaging techniques by vivid examples through numerical and experimental demonstrations and provides computer codes and practical uses. This book is unique...
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Focusing on most advanced analytical and numerical modeling, this book connects 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 treating radar scattering and imaging simultaneously.
Autoren-Porträt von Kun-Shan Chen
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).
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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 (2010-2014), 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 (2008-2010). 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 (2014-2019) and has been a member of the editorial board of the IEEE Access since 2020. He is a Fellow of IEEE.
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 (2010-2014), 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 (2008-2010). 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 (2014-2019) 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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Bibliographische Angaben
- Autor: Kun-Shan Chen
- 2020, 1. Auflage, 339 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1351011561
- ISBN-13: 9781351011563
- Erscheinungsdatum: 19.11.2020
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Größe: 70 MB
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