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

Lorenzo Bruzzone; Sebastiano Bruno Serpico

doi:10.1117/12.331895

4 December 1998 New search algorithm for feature selection in high-dimensional remote sensing images

Lorenzo Bruzzone, Sebastiano Bruno Serpico

Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331895
Event: Remote Sensing, 1998, Barcelona, Spain

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.

Citation Download Citation

Lorenzo Bruzzone and Sebastiano Bruno Serpico "New search algorithm for feature selection in high-dimensional remote sensing images", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331895

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