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.