Feature extraction from hyperspectral images using learned edge structures

In this letter, a novel edge-preserving filtering based approach is proposed for feature extraction of hyperspectral images, which consists of the following steps. First, the dimension of the hyperspectral image is reduced with an averaging based method. Then, the resulting features are obtained by performing edge-preserving filtering on the dimension reduced image, in which a learned edge detection map serves as one of the major cues in the filtering process. The advantage of the proposed method is that it makes full use of the learned edge information in the feature extraction process, and thus, able to improve the performance with respect to other traditional feature extraction methods. Experiments on two real hyperspectral data sets demonstrate the outstanding performance of the proposed method especially when the number of training samples is limited.