Classification of hyperspectral data using best-bases feature extraction algorithms

Shailesh Kumar; Joydeep Ghosh; Melba M. Crawford

doi:10.1117/12.380589

30 March 2000 Classification of hyperspectral data using best-bases feature extraction algorithms

Shailesh Kumar, Joydeep Ghosh, Melba M. Crawford

Proceedings Volume 4055, Applications and Science of Computational Intelligence III; (2000) https://doi.org/10.1117/12.380589
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Shailesh Kumar, Joydeep Ghosh, and Melba M. Crawford "Classification of hyperspectral data using best-bases feature extraction algorithms", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380589

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