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Proceedings Paper

Classification of hyperspectral data using best-bases feature extraction algorithms
Author(s): Shailesh Kumar; Joydeep Ghosh; Melba M. Crawford
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Paper Abstract

Mapping landcover type from airborne/spaceborne sensors is an important classification problem in remote sensing. Due to advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in more than 100 bands, each of which measures the integrated response of a target over a narrow window of the electromagnetic spectrum. The bands are ordered by their wavelengths and spectrally adjacent bands are generally statistically correlated.

Paper Details

Date Published: 30 March 2000
PDF: 12 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380589
Show Author Affiliations
Shailesh Kumar, Univ. of Texas at Austin (United States)
Joydeep Ghosh, Univ. of Texas at Austin (United States)
Melba M. Crawford, Univ. of Texas at Austin (United States)


Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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