Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation

TL;DR

This paper introduces an unsupervised band selection method for hyperspectral images using multi-dictionary sparse representation, effectively reducing spectral dimensionality and improving classification performance.

Contribution

It proposes a novel multi-dictionary sparse representation framework for unsupervised band selection in hyperspectral images, enhancing classification accuracy.

Findings

Outperforms existing band selection methods in classification tasks

Reduces spectral dimensionality effectively

Improves computational efficiency

Abstract

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.