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Real-Time Progressive Hyperspectral Image Processing: Endmember Finding and Anomaly Detection - Hardcover
Inhaltsangabe
Overview and Introduction.- Part I: Preliminaries.- Linear Spectral Mixture Analysis.- Finding Endmembers in Hyperspectral Imagery.- Linear Spectral Unmixing with Three Criteria, Least Squares Error, Simplex Volume and Orthogonal Projection.- Hyperspectral Target Detection.- Part II: Sample-wise Sequential Processes for Finding Endmembers.- Abundance-Unconstrained Sequential Endmember Finding Algorithms: Orthogonal Projection.- Fully Abundance-Constrained Sequential Endmember Finding Algorithms: Simplex Volume Analysis.- Partially Abundance Non-Negativity-Constrained Endmember Finding Algorithms: Convex Cone Volume Analysis.- Fully Abundance-Constrained Sequential Linear Spectral Mixture Analysis for Finding Endmembers.- Part III: Sample-Wise Progressive Processes for Finding Endmembers.- Abundance-Unconstrained Progressive Endmember Finding Algorithms: Orthogonal Projection.- Fully Abundance-Unconstrained Progressive Endmember Finding Algorithms: Simplex Volume Analysis.- Partially Abundance Non-Negativity-Constrained Progressive Endmember Finding Algorithms: Convex Cone Volume Analysis.- Sully Abundance-Constrained Progressive Linear Spectral Mixture Analysis for Finding Endmembers.- Part IV: Sample-Wise Progressive Unsupervised Target Detection.- Progressive Anomaly Detection.- Progressive Adaptive Anomaly Detection.- Progressive Window-Based Anomaly Detection.- Progressive Subpixel Target Detectio n and Classification.
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Über die Autorin bzw. den Autor
Chein-I Chang is a Professor with the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. His Remote Sensing Signal and Image Processing Laboratory (RSSIPL) conducts research in designing and developing signal processing algorithms for multispectral and hyperspectral imaging, medical imaging.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.
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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.
- 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
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Real-Time Progressive Hyperspectral Image Processing: Endmember Finding and Anomaly Detection
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Real-Time Progressive Hyperspectral Image Processing
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Springer New York, Springer New York Mär 2016, 2016
ISBN 10: 1441961860
ISBN 13: 9781441961860
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Buch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 652 pp. Englisch. Artikel-Nr. 9781441961860
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Real-Time Progressive Hyperspectral Image Processing : Endmember Finding and Anomaly Detection
Verlag:
Springer New York, Springer New York, 2016
ISBN 10: 1441961860
ISBN 13: 9781441961860
Neu
Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9781441961860
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Endmember Finding and Target Detection in Hyperspectral Imagery: Algorithm Architecture and Implementation
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Hardcover. Zustand: Brand New. 623 pages. 9.50x6.50x2.00 inches. In Stock. Artikel-Nr. x-1441961860
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Real-Time Progressive Hyperspectral Image Processing: Endmember Finding and Anomaly Detection
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Zustand: New. Focusing on the architecture and implementation of algorithms, this volume presents real-time and causal processing implementation, as well as architectures of FPGA design and parallel processing. Readers will find coverage of applications to both imaging and medical imaging. Num Pages: 490 pages, 81 black & white illustrations, 359 colour illustrations, 65 black & white tables, 12 col. BIC Classification: TTBM; UYT. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 0. . . 2016. 1st ed. 2016. Hardcover. . . . . Books ship from the US and Ireland. Artikel-Nr. V9781441961860
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