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Hyperspectral Imaging: Techniques for Spectral Detection and Classification | 
 enlarge | Author: Chein-i Chang
Publisher: Springer
Category: Book
List Price: $94.00
Buy New: $75.17
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Rating:  2 reviews
Sales Rank: 1236625
Media: Hardcover
Edition: 1
Pages: 367
Number Of Items: 1
Shipping Weight (lbs): 1.6
Dimensions (in): 9 x 6.1 x 1.1
ISBN: 0306474832
Dewey Decimal Number: 621.3678
EAN: 9780306474835
ASIN: 0306474832
Publication Date: July 31, 2003
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Product Description
Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.
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| Customer Reviews:
 Worst quality I have EVER seen! December 17, 2006
J. Thompson
2 out of 2 found this review helpful
For a book about cutting edge, remote sensing techniques, the quality of the printing of this book is appalling! Images are blurry, and their text captions are often unreadable. Even the print quality of the normal text itself is often poor and misaligned - news papers have better quality than this book!
 Solid and useful technical content January 5, 2009
Eisteddfod
This is a very good account of signal processing methods for detection/classification stemming from the author's diligent work over a 15-year period. Though this is not my particular application of expertise I am sufficiently familiar with signal processing theory/methods to recognize the merit of this book. As of date you cannot view pages from this book on Amazon, so here is some help from the Springer website.
Table of contents
1. Introduction.
Part I: Hyperspectral Measures.
2. Hyperspectral measures for spectral characterization.
Part II: Subpixel Detection.
3. Target abundance-constrained subpixel detection.
4. Target signature-constrained subpixel detection: linearly constrained minimum variance (LCMV).
5. Automatic subpixel detection (unsupervised subpixel detection).
6. Anomaly detection.
7. Sensitivity of subpixel detection.
Part III: Unconstrained Mixed Pixel Classification.
8. Unconstrained Mixed Pixel Classification: least squares subspace projection.
9. A quantitative analysis of mixed-to-pure pixel conversion.
Part IV: Constrained Mixed Pixel Classification.
10. Target abundance-constrained mixed pixel classification (TACMPC)
11. Target signature-constrained mixed pixel classification (TSCMPC): LCMV multiple target classifiers.
12. Signature-constrained mixed pixel classification (TSCMPC): Linearly constrained discriminant analysis (LCDA).
Part V: Automatic Mixed Pixel Classification (AMPC).
13. Automatic mixed pixel classification (AMPC): unsupervised mixed pixel classification.
14. Automatic mixed pixel classification (AMPC): anomaly classification
15. Automatic mixed pixel classification (AMPC): linear spectral random mixture analysis (LSRMA).
16. Automatic mixed pixel classification (AMPC): projection pursuit.
17. Estimation of virtual dimensionality of hyperspectral imagery.
18. Conclusion and further techniques.
Glossary.
References.
Index.
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