Unsupervised Hyperspectral Band Selection by Dominant Set Extraction

Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands. Although many efforts have been made to this topic, the structural information has not yet been well exploited during band selection, and there are still several deficiencies in search strategies, leaving room for further improvement. This paper tackles the unsupervised hyperspectral band selection problem from a global perspective and proposes a novel method claiming the following main contributions: structure-aware measures for band informativeness and independence; and a graph formulation of band selection allowing for an efficient integrated search by means of dominant set extraction. Experiments on three real hyperspectral images demonstrate the superiority of the proposed band selector in comparison with benchmark methods.