Real-time progressive hyperspectral image processing [electronic resource] : endmember finding and anomaly detection / Chein-I Chang.
Chang, Chein-I, author.
2016
TA1637
Available Online

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Title Real-time progressive hyperspectral image processing [electronic resource] : endmember finding and anomaly detection / Chein-I Chang.
Author Chang, Chein-I, author.
ISBN 9781441961877 (electronic book)
1441961879 (electronic book)
9781441961860
DOI https://doi.org/10.1007/978-1-4419-6187-7
Published [New York] : Springer, [2016]
Copyright ©2016
Language English
Description 1 online resource
Item Number 10.1007/978-1-4419-6187-7 doi
Call Number TA1637
Dewey Decimal Classification 621.3678
Summary 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. It includes preliminary background which is essential to those who work in hyperspectral imaging area. It develops sequential and progressive algorithms for finding endmembers as they relate to real time hyperspectral image processing. It designs algorithms for anomaly detection from causality and real time perspectives and investigates the effects of causality and real-time processing in anomaly detection.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (viewed March 30, 2016)
Available in Other Form Print version: 9781441961860
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Overview and introduction
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
Fully geometric-constrained sequential endmember finding: simplex volume analysis-based N-FINDR
Partially geometric-constrained sequential endmember finding: convex cone volume analysis
Geometric-unconstrained sequential endmember finding: orthogonal projection analysis
Fully abundance-constrained sequential endmember finding: linear spectral mixture analysis
Fully geometric-constrained progressive endmember finding: growing simplex volume analysis
Partially geometric-constrained progressive endmember finding: growing convex cone volume analysis
Geometric-unconstrained progressive endmember finding: orthogonal projection analysis
Endmember finding algorithms: comparative studies and analyses
Anomaly detection characterization
Anomaly discrimination and categorization
Anomaly detection and background suppression
Multiple window anomaly detection
Anomaly detection using causal sliding windows.

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