Band Selection Through Redundancy Elimination

Abstract

In this chapter, we discuss a method for selection of a few specific bands to accomplish a faster fusion of hyperspectral images, without much sacrificing the quality of the result of fusion. We present an information theoretic strategy for choosing specific image bands of the hyperspectral data cube using only the input hyperspectral image. The selected subset of bands can then be fused using any existing pixel-based fusion technique as the band selection process is independent of the method of fusion. The objective of this chapter lies in providing a much faster scheme for hyperspectral image visualization with a minimal degradation in the fusion quality through selection of specific bands. The band selection process should be computationally inexpensive, and yet, should be able to generate comparable quality fusion results for the given fusion technique. The subsequent section describes the scheme of entropy-based band selection which is a generic scheme for the data containing a large number of bands. The hyperspectral data consist of a spectrally ordered set of contiguous bands. We exploit this characteristic, and develop a model for measurement of the similarity across bands. We also provide a couple of theorems for the savings in computation for this special case of band selection.