A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing
Each pixel in the hyperspectral unmixing process is modeled as a linear combination of endmembers, which can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, the limitation of Gaussian random variables on its computational co...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/8471024 |
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| _version_ | 1849306750105157632 |
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| author | Su Xu Xiping He |
| author_facet | Su Xu Xiping He |
| author_sort | Su Xu |
| collection | DOAJ |
| description | Each pixel in the hyperspectral unmixing process is modeled as a linear combination of endmembers, which can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, the limitation of Gaussian random variables on its computational complexity or sparsity affects the efficiency and accuracy. This paper proposes a novel approach for the optimization of measurement matrix in compressive sensing (CS) theory for hyperspectral unmixing. Firstly, a new Toeplitz-structured chaotic measurement matrix (TSCMM) is formed by pseudo-random chaotic elements, which can be implemented by a simple hardware; secondly, rank revealing QR factorization with eigenvalue decomposition is presented to speed up the measurement time; finally, orthogonal gradient descent method for measurement matrix optimization is used to achieve optimal incoherence. Experimental results demonstrate that the proposed approach can lead to better CS reconstruction performance with low extra computational cost in hyperspectral unmixing. |
| format | Article |
| id | doaj-art-239ce28fc2144f25b4f7f188c35a4f02 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-239ce28fc2144f25b4f7f188c35a4f022025-08-20T03:54:57ZengWileyJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/84710248471024A Novel Measurement Matrix Optimization Approach for Hyperspectral UnmixingSu Xu0Xiping He1College of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing 400067, ChinaCollege of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing 400067, ChinaEach pixel in the hyperspectral unmixing process is modeled as a linear combination of endmembers, which can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, the limitation of Gaussian random variables on its computational complexity or sparsity affects the efficiency and accuracy. This paper proposes a novel approach for the optimization of measurement matrix in compressive sensing (CS) theory for hyperspectral unmixing. Firstly, a new Toeplitz-structured chaotic measurement matrix (TSCMM) is formed by pseudo-random chaotic elements, which can be implemented by a simple hardware; secondly, rank revealing QR factorization with eigenvalue decomposition is presented to speed up the measurement time; finally, orthogonal gradient descent method for measurement matrix optimization is used to achieve optimal incoherence. Experimental results demonstrate that the proposed approach can lead to better CS reconstruction performance with low extra computational cost in hyperspectral unmixing.http://dx.doi.org/10.1155/2017/8471024 |
| spellingShingle | Su Xu Xiping He A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing Journal of Control Science and Engineering |
| title | A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing |
| title_full | A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing |
| title_fullStr | A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing |
| title_full_unstemmed | A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing |
| title_short | A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing |
| title_sort | novel measurement matrix optimization approach for hyperspectral unmixing |
| url | http://dx.doi.org/10.1155/2017/8471024 |
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