Representativeness and Redundancy-Based Band Selection for Hyperspectral Image Classification`

Abstract

ABSTRACT Hyperspectral band selection (BS) aims to select a subset of bands from the original image cube for subsequent tasks, such as pixel classification. In this paper, we propose a novel unsupervised BS method, termed the representativeness and redundancy-based BS (RRBS) method, by measuring the representativeness and redundancy of the selected bands. The intuitive motivation is to find a subset of bands, which represents the dataset and has low redundancy. The desired bands are obtained sequentially. In each round of lookup, two novel selection criteria based on orthogonal subspace projection are designed for searching the bands that not only well represent the unselected bands but also lowly correlate with the selected bands. Additionally, kernel tricks are used to develop a nonlinear version of the linear selection criteria. Both the linear and nonlinear selection criteria can explicitly evaluate the representativeness and redundancy simultaneously, and they are also robust to noisy bands. The experimental results verify that the proposed method yields excellent classification performance even selecting very a limited number of bands.