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2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2018
This paper explores the use of column subset selection (CSS) for unsupervised band subset selection (BSS) in hyperspectral imaging. CSS is the problem of selecting the most independent columns of a matrix. Many deterministic and randomized algorithms have been proposed in the literature for CSS.
Maher Aldeghlawi, Miguel Velez-Reyes
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This paper explores the use of column subset selection (CSS) for unsupervised band subset selection (BSS) in hyperspectral imaging. CSS is the problem of selecting the most independent columns of a matrix. Many deterministic and randomized algorithms have been proposed in the literature for CSS.
Maher Aldeghlawi, Miguel Velez-Reyes
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Endoscopic Sheffield Index for Unsupervised In Vivo Spectral Band Selection
2014Endoscopic procedures provide important information about the internal patient anatomy but are currently restricted to a 2D texture analysis of the visible organ surfaces. Spectral imaging has high potential in generating valuable complementary information about the molecular tissue composition but suffers from long image acquisition times.
Sebastian J. Wirkert +8 more
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Unsupervised Band Selection Based on Group-Based Sparse Representation
2017Band 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.
Hung-Chang Chien +2 more
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Boltzmann Entropy-Based Unsupervised Band Selection for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2019Band selection for hyperspectral images helps improve the efficiency of data processing and even the accuracy of classification. It is to reduce the dimensionality of a hyperspectral image by selecting representative bands. In such a process, the quantification of band similarity is the fundamental issue, and it is usually achieved by using an ...
Peichao Gao +3 more
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Unsupervised Hyperspectral Band Selection Method Based on Low-Rank Representation
2019In order to reduce the spectral redundancy of hyperspectral remote sensing images and reduce the computational complexity of subsequent processing, an unsupervised hyperspectral image band selection algorithm based on low-rank representation (LRBS) was proposed in this paper. First, a low-rank representation of the hyperspectral image is proposed and a
Chunyan Yu +3 more
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Unsupervised band selection for multispectral images using information theory
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004José MartÃnez Sotoca +2 more
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Unsupervised Hyperspectral Band Selection via Hybrid Graph Convolutional Network
IEEE Transactions on Geoscience and Remote Sensing, 2022Chunyan Yu +5 more
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Robust Dual Graph Self-Representation for Unsupervised Hyperspectral Band Selection
IEEE Transactions on Geoscience and Remote Sensing, 2022Yongshan Zhang +2 more
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