Real-time progressive hyperspectral image processing : endmember finding and anomaly detection / Chein-I Chang
Material type:
TextPublication details: New York : Springer, 2016.Description: xix, 623 pages : illustrations ; 25 cmISBN: - 9781441961860
- 621.3678 23 C456
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 621.3678 C456 (Browse shelf(Opens below)) | Available | 137590 |
Browsing ISI Library, Kolkata shelves Close shelf browser (Hides shelf browser)
| No cover image available | ||||||||
| 621.3678 B873 Mathematics in remote sensing | 621.3678 C455 Hyperspectral imaging: techniques for spectral detection and classification/ | 621.3678 C456 Remote sensing and geographical information system / | 621.3678 C456 Real-time progressive hyperspectral image processing : | 621.3678 C749 Assessing the accuracy of remotely sensed data | 621.3678 C883 Introduction to remote sensing | 621.3678 Ei34 Hyperspectral remote sensing/ |
Includes bibliographical references and index.
1. Overview and introduction --
2. Linear spectral mixture analysis --
3. Finding endmembers in hyperspectral imagery --
4. Linear spectral unmixing with three criteria, least squares error, simplex volume and orthogonal projection --
5. Hyperspectral target detection --
6. Fully geometric-constrained sequential endmember finding: simplex volume analysis-based N-FINDR --
7. Partially geometric-constrained sequential endmember finding: convex cone volume analysis --
8. Geometric-unconstrained sequential endmember finding: orthogonal projection analysis --
9. Fully abundance-constrained sequential endmember finding: linear spectral mixture analysis --
10. Fully geometric-constrained progressive endmember finding: growing simplex volume analysis --
11. Partially geometric-constrained progressive endmember finding: growing convex cone volume analysis --
12. Geometric-unconstrained progressive endmember finding: orthogonal projection analysis --
13. Endmember finding algorithms: comparative studies and analyses --
14. Anomaly detection characterization --
15. Anomaly discrimination and categorization --
16. Anomaly detection and background suppression --
17. Multiple window anomaly detection --
18. Anomaly detection using causal sliding windows --
19. Conclusions.
The book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Two new concepts of real time hyperspectral image processing, Progressive Hyperspectral Imaging (PHSI) and Recursive Hyperspectral Imaging (RHSI) have recently emerged. 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 the 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.
There are no comments on this title.
