Abstract
Band selection (BS) is one of the important topics in hyperspectral image data analysis. How to search the representative bands that can effectively represent the image with lower inter-band redundancy is an long-term issue. Recently, the sparse representation (SR) was used to solve BS problem, called SR-BS. It aimed to find a set of representative bands that can represent the whole bands based on the minimization of reconstructed error in SR. However, those SR-BS methods suffer from an issue about higher complexity in the optimization process, even though the greedy strategy, such as orthogonal matching pursuit (OMP) algorithm, is used to accelerate them. Another issue is that the bands selected by SR-BS may not be really complementary since the homogeneity of adjacent spectral bands is not considered. To make SR-BS more efficient, in this paper, we model the BS problem as group sparse representation (GSR) problem where the dictionary matrix (i.e., all spectral bands) are pre-clustered to several non-overlapping groups based on the spectral similarity. Later, we adopt group orthogonal matching pursuit (GOMP) algorithm to solve the optimization problem. We named the proposed approach as GOMP-BS. Since GOMP-BS picks bands under group structure, it not only reaches higher computational efficiency but also makes the selected bands more less redundant. The experiments show that GOMP-BS achieves higher classification performance when the number of selected bands is low and requires less computation time than OMP-based methods.
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Chien, HC., Lai, CH., Liu, KH. (2017). Unsupervised Band Selection Based on Group-Based Sparse Representation. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_26
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DOI: https://doi.org/10.1007/978-3-319-54407-6_26
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