Abstract
Band selection is a direct and effective method to reduce the spectral dimension, which is one of popular topics in hyperspectral remote sensing. Recently, a number of methods were proposed to deal with the band selection problem. Motivated by the previous sparse representation methods, we present a novel framework for band selection based on multi-dictionary sparse representation (MDSR). By obtaining the sparse solutions for each band vector and the corresponding dictionary, the contribution of each band to the raw image is derived. In terms of contribution, the appropriate band subset is selected. Although the number of dictionaries is increasing, the efficiency of the algorithm is much higher than the previous due to the reduction of the dictionary self-learning process. Five state-of-the-art band selection methods are compared with the MDSR on three widely used hyperspectral datasets (Salinas-A, Pavia-U, and Indian Pines). Experimental results show that the MDSR achieves marginally better performance in hyperspectral image classification and better performance in average correlation coefficient and computational time.
Highlights
H YPERSPECTRAL remote sensing is a critical research area for remote sensing applications [1]
In this paper we propose a novel unsupervised band selection algorithm for hyperspectral image processing that utilizes the sparsity of the input sample vector
Experimental results on three widely used hyperspectral image classification datasets show that our proposed algorithm achieves superior performance and significantly outperforms other state-of-the-art unsupervised band selection methods
Summary
H YPERSPECTRAL remote sensing is a critical research area for remote sensing applications [1]. It is necessary to select and reduce the spectral dimension for efficient hyperspectral image applications. One is robust feature extraction and the other is specific band selection ( called feature selection) The former usually generates a low-dimensional spectral data by using a transformation matrix based on a certain criteria. Aforementioned methods are classical unsupervised or supervised feature extraction methods, and they inevitably change the physical meaning of the original bands The latter is to identify the best subset of bands from all the original bands based on an adaptive selection criterion [8]. In this paper we propose a novel unsupervised band selection algorithm for hyperspectral image processing that utilizes the sparsity of the input sample vector. Experimental results on three widely used hyperspectral image classification datasets show that our proposed algorithm achieves superior performance and significantly outperforms other state-of-the-art unsupervised band selection methods.
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