Hyperspectral imaging : techniques for spectral detection and classification / Chein-I Chang.
Language: İngilizce Publisher: New York : Kluwer Academic/Plenum Publishers, 2003Description: xvi, 370 p. : ill. ; 24 cmISBN:- 0306474832
- G70.4 .C46 2003
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| G70.212 .H36 2003 The design and implementation of geographic information systems / | G70.212 .K67 2001 The GIS book / | G 70.215.A7 K393 2007 Yerel yönetimlerde coğrafi bilgi sistemleri: Türkiye uygulamaları. / | G70.4.C46 2003 Hyperspectral imaging : techniques for spectral detection and classification / | G70.4 .G63 1999 Civil, commercial, and international remote sensing systems and geoprocessing / | G70.4 .L345 2003 Signal theory methods in multispectral remote sensing / | G76.5.T8 O76 2000 v.1 Osmanlı coğrafya literatürü tarihi = History of geographical literature during the Ottoman period / |
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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