Successive Clustering-Based Outlier Resistant Band Selection Method for Hyperspectral Images With Spatial Information Difference Metrics

In hyperspectral classification applications, band selection (BS) is an effective preprocessing method that reduces image redundancy without changing the original data. The property whereby different objects can be spatially separated is used for image classification, but BS methods based on quantitation of this property have not gotten enough attention. A cluster-based BS method that uses the dilation distances (DDs) with respect to the metric of spatial distances has been proposed, but the DD is strongly affected by outliers and calculating DD is time-consuming. Moreover, there is a mismatch between DD and the method of clustering and selecting representative band. In this letter, we propose a BS method based on pixel sorting-feature-based DD (SFDD) to accurately determine spatial information differences (SIDs) metric and design a method of successive clustering as well as a method of representative BS to match the features of this metric. We optimize the method to calculate the SFDD to reduce the time needed for it. In contrast to most BS methods, the bands selected by our method have a large SID among them such that objects at different positions are clearly differentiated in the spectral dimension after dimension reduction. The results of experiments showed that the proposed approach provides results that are competitive with those of several state-of-the-art methods.