Discriminative weighted band selection via one-class SVM for hyperspectral imagery
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Abstract: In the task of hyperspectral image classification, band selection is often adopted to select a subset of informative bands to reduce the computation and storage cost. We propose a supervised band selection method which allows calculation of a discriminative weight for each band. Specifically, we consider discriminative bands as those that contribute more positive scores to a one-class classifier than those for other classes during the training stage. Based on this observation, we learn discriminative a band weight vector for each class, then bands with larger discriminative weights can be selected. Our method can be efficiently solved in one-class SVM framework. Experimental results demonstrate the effectiveness of our method.
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Proceedings
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Artificial Intelligence and Image Processing not elsewhere classified