Overview
- Includes preliminary background which is essential to those who work in hyperspectral imaging area
- Develops sequential and progressive algorithms for finding endmembers as they relate to real time hyperspectral image processing
- Designs algorithms for anomaly detection from causality and real time perspectives and investigates the effects of causality and real-time processing in anomaly detection
- Includes supplementary material: sn.pub/extras
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Table of contents (20 chapters)
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Front Matter
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Sample-Wise Sequential Processes for Finding Endmembers
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Front Matter
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Sample-Wise Progressive Processes for Finding Endmembers
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Front Matter
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Hyperspectral Anomaly Detection
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Front Matter
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Keywords
About this book
The book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Recently, two new concepts of real time hyperspectral image processing, Progressive HyperSpectral Imaging (PHSI) and Recursive HyperSpectral Imaging (RHSI). Both of these can be used to design algorithms and also form an integral part of real time hyperpsectral image processing. This book focuses on progressive nature in algorithms on their real-time and causal processing implementation in two major applications, endmember finding and anomaly detection, both of which are fundamental tasks in hyperspectral imaging but generally not encountered in multispectral imaging. This book is written to particularly address PHSI in real time processing, while a book, Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation (Springer 2016) can be considered as its companion book.
Authors and Affiliations
About the author
Dr. Chang has published over 150 referred journal articles, including more than 50 papers in the IEEE Transaction on Geoscience and Remote Sensing alone and four patents with several pending on hyperspectral image processing. He authored two books, Hyperspectral Imaging: Techniques for Spectral Detection and Classification (Kluwer Academic Publishers, 2003) and Hyperspectral Data Processing: Algorithm Design and Analysis (Wiley, 2013). He also edited two books, Recent Advances in Hyperspectral Signal and Image Processing (Trasworld Research Network, India, 2006) and Hyperspectral Data Exploitation: Theory and Applications (John Wiley & Sons, 2007) and co-edited, with A. Plaza, a book on High Performance Computing in Remote Sensing (CRC Press, 2007).
Dr. Chang has received his Ph.D. in Electrical Engineering from University of Maryland, College Park, Maryland. He is a Fellow of IEEE and SPIE with contributions to hyperspectral image processing.
Bibliographic Information
Book Title: Real-Time Progressive Hyperspectral Image Processing
Book Subtitle: Endmember Finding and Anomaly Detection
Authors: Chein-I Chang
DOI: https://doi.org/10.1007/978-1-4419-6187-7
Publisher: Springer New York, NY
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Science+Business Media, LLC 2016
Hardcover ISBN: 978-1-4419-6186-0Published: 23 March 2016
Softcover ISBN: 978-1-4939-7925-7Published: 24 April 2018
eBook ISBN: 978-1-4419-6187-7Published: 22 March 2016
Edition Number: 1
Number of Pages: XXIII, 623
Number of Illustrations: 75 b/w illustrations, 256 illustrations in colour
Topics: Signal, Image and Speech Processing, Image Processing and Computer Vision, Pattern Recognition, Biometrics