  Hyperspectral images have far more spectral bands than ordinary multispectral
images. Rich band information provides more favorable conditions for the
tremendous applications. However, significant increase in the dimensionality of
spectral bands may lead to the curse of dimensionality, especially for
classification applications. Furthermore, there are a large amount of redundant
information among the raw image cubes due to water absorptions, sensor noises
and other influence factors. Band selection is a direct and effective method to
remove redundant information and reduce the spectral dimension for decreasing
computational complexity and avoiding the curse of dimensionality. In this
paper, we present a novel learning framework for band selection based on the
idea of sparse representation. More specifically, first each band is
approximately represented by the linear combination of other bands, then the
original band image can be represented by a multi-dictionary learning
mechanism. As a result, a group of weights can be obtained by sparse
optimization for all bands. Finally, the specific bands will be selected, if
they get higher weights than other bands in the representation of the original
image. Experimental results on three widely used hyperspectral datasets show
that our proposed algorithm achieves better performance in hyperspectral image
classification, when compared with other state-of-art band selection methods.
