12 April 2004 Feature selection from high-dimensional hyperspectral and polarimetric data for target detection
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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.
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Xue-Wen Chen, Xue-Wen Chen, David P. Casasent, David P. Casasent, } "Feature selection from high-dimensional hyperspectral and polarimetric data for target detection", Proc. SPIE 5437, Optical Pattern Recognition XV, (12 April 2004); doi: 10.1117/12.541414; https://doi.org/10.1117/12.541414

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