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Proceedings Paper

New search algorithm for feature selection in high-dimensional remote sensing images
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Paper Abstract

A new sub-optimal search strategy suitable for feature selection in high-dimensional remote-sensing images (e.g. images acquired by hyperspectral sensors) is proposed. Such a strategy is based on a search for constrained local extremes in a discrete binary space. In particular, two different algorithms are presented that achieve a different trade-off between effectiveness of selected features and computational cost. The proposed algorithms are compared with the classical sequential forward selection (SFS) and sequential forward floating selection (SFFS) sub-optimal techniques: the first one is a simple but widely used technique; the second one is considered to be very effective for high-dimensional problems. Hyperspectral remote-sensing images acquired by the AVIRIS sensor are used for such comparisons. Experimental results point out the effectiveness of the presented algorithms.

Paper Details

Date Published: 4 December 1998
PDF: 8 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331895
Show Author Affiliations
Lorenzo Bruzzone, Univ. of Genoa (Italy)
Sebastiano Bruno Serpico, Univ. of Genoa (Italy)

Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

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