Hyperspectral Band Selection via Rank Minimization

Abstract

Band selection is an important preprocessing technique for hyperspectral imagery, through which a subset of critical and representative spectral bands can be selected from a raw image cube for compact yet effect representation. Among the valid selection strategies, performing band selection in an unsupervised manner is usually considered more general due to its application-independent characteristic. This letter proposed a novel unsupervised hyperspectral band selector that can capture the interband redundancy nature of hyperspectral images through low-rank modeling. Experiments on three real-world hyperspectral data sets demonstrated that the proposed band selector can generate band subsets suitable in the context of hyperspectral pixel classification.