Customer Reviews

Hyperspectral Imaging: Techniques for Spectral Detection and Classification

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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!

I am reviewing this book as a possibility for use in a master's-level class. The one advantage I see of this book is that it provides a large list of references for each subject. However, the pictures are very poor quality, and the text is difficult to read. Furthermore, the descriptions of analytical techniques are often worse than those presented in the original journal articles. Hence, I plan to use the journal articles referenced in this book as a basis for the class, but to return the actual book itself. It's a shame to see a text with this much potential end up looking this poor in quality.

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.