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Proceedings Paper

Feature selection technique for classification of hyperspectral AVIRIS data
Author(s): Sylvia S. Shen; Bonnie Y. Trang
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Paper Abstract

A new generation of remote sensing instruments, called imaging spectrometers, are designed to collect image data in literally hundreds of spectral channels simultaneously, providing significantly enhanced amounts of information as compared to existing systems for studying biophysical processes. The advantage of such high-dimensional data comes at the cost of increased data complexity. The volume and complexity of the new data, in turn, presents a challenge to the traditional image analysis methods and requires that new approaches be developed to allow rapid and effective analysis of the imagery. This paper describes a technique which reduces the data dimensionality, while retaining sufficient pertinent information that the original high-dimensional signals provide for class separation. Results of applying this technique to 224-band AVIRIS data are presented.

Paper Details

Date Published: 1 December 1991
PDF: 6 pages
Proc. SPIE 1567, Applications of Digital Image Processing XIV, (1 December 1991); doi: 10.1117/12.50814
Show Author Affiliations
Sylvia S. Shen, Lockheed Palo Alto Research Labs. (United States)
Bonnie Y. Trang, Lockheed Palo Alto Research Labs. (United States)

Published in SPIE Proceedings Vol. 1567:
Applications of Digital Image Processing XIV
Andrew G. Tescher, Editor(s)

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