Information-Assisted Density Peak Index for Hyperspectral Band Selection

Abstract

Band selection has become an effective method to reduce hyperspectral dimensionality. In this letter, an information-assisted density peak index (IaDPI) is proposed to prioritize the bands. Based on a clustering method by finding density peaks, IaDPI introduces the intraband information entropy into the local density and intercluster distance to ensure cluster centers with a high quality. Also, the band distance is integrated with channel proximity to control the compactness of local density. Owing to the intraband entropy and the interband weighted dissimilarity, the selected band set with top-ranked IaDPI scores can hold high local density, clear global distinction, and good informative quality. Experimental results on real hyperspectral data indicate the advantages of the proposed IaDPI in good selection quality, robust noise immunity, and high classification accuracy.