PROBLEM-BASED BAND SELECTION FOR HYPERSPECTRAL IMAGES

This paper addresses the band selection of a hyperspectral image. Considering a binary classification, we devise a method to choose the more discriminating bands for the separation of the two classes involved, by using a simple algorithm: single-layer neural network. After that, the most discriminative bands are selected, and the resulting reduced data set is used in a more powerful classifier, namely, stacked denoising autoencoder. Besides its simplicity, the advantage of this method is that the selection of features is made by an algorithm similar to the classifier to be used, and not focused only on the separability measures of the data set. Results indicate the decrease of overfitting for the reduced data set, when compared to the full data architecture.