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

by   Fei Li, et al.

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.



page 5


Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks

In recent years, Hyperspectral Imaging (HSI) has become a powerful sourc...

Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectr...

An automatic bad band preremoval algorithm for hyperspectral imagery

For most hyperspectral remote sensing applications, removing bad bands, ...

A block-based inter-band predictor using multilayer propagation neural network for hyperspectral image compression

In this paper, a block-based inter-band predictor (BIP) with multilayer ...

Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images

In the small target detection problem a pattern to be located is on the ...

SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection

The band selection in the hyperspectral image (HSI) data processing is a...

Fusion of heterogeneous bands and kernels in hyperspectral image processing

Hyperspectral imaging is a powerful technology that is plagued by large ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.