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Proceedings Paper

Feature selection from high-dimensional hyperspectral and polarimetric data for target detection
Author(s): Xue-Wen Chen; David P. Casasent
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Paper Abstract

Hyperspectral and polarimetric data contain spectral response information that provides detailed descriptions of an object. These new sensor data are useful in automatic target recognition applications. However, such high-dimensional data introduce problems due to the curse of dimensionality, the need to reduce the number of features used to accommodate realistic small training set sizes, and the need to employ discriminatory features and still achieve good generalization (comparable training and test set performance). In this paper, we evaluate both hyperspectral and polarimetric feature sets and identify features useful for distinguishing targets from background. Various feature selection algorithms are assessed in terms of the goodness of the selected features and computation time. Our results show that (1) the integration of branch and bound algorithm and floating forward selection algorithm is promising for hyperspectral and polarimetric target detection applications; and (2) the combination of both hyperspectral and polarimetric features yields significantly better classification results than either hyperspectral or polarimetric features alone.

Paper Details

Date Published: 12 April 2004
PDF: 8 pages
Proc. SPIE 5437, Optical Pattern Recognition XV, (12 April 2004); doi: 10.1117/12.541414
Show Author Affiliations
Xue-Wen Chen, Univ. of Kansas (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 5437:
Optical Pattern Recognition XV
David P. Casasent; Tien-Hsin Chao, Editor(s)

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