16 September 2003 Mine and vehicle detection in hyperspectral image data: waveband selection
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Abstract
Hyperspectral (HS) data contains 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 introduces problems due to the curse of dimensionality, the need to reduce the number of features (λ responses) 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). HS sensors produce high-dimensional data; this is characterized by a training set size (Ni) per class that is less than the number of input features (NF). A new high-dimensional generalized discriminant (HDGD) feature extraction algorithm and a new modified branch and bound (MBB) feature selection algorithm are described and compared to other feature reduction methods for two HS target detection applications (mine and vehicle detection). Both space and spectral parameters are adapted. A new blob-coloring hit-miss transform is introduced.
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David P. Casasent, David P. Casasent, Xue-Wen Chen, Xue-Wen Chen, } "Mine and vehicle detection in hyperspectral image data: waveband selection", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); doi: 10.1117/12.497497; https://doi.org/10.1117/12.497497
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