Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization
Subpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature an...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/2683248 |
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| _version_ | 1832565383415988224 |
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| author | Shuhan Chen Xiaorun Li Liaoying Zhao |
| author_facet | Shuhan Chen Xiaorun Li Liaoying Zhao |
| author_sort | Shuhan Chen |
| collection | DOAJ |
| description | Subpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature and local convergence of the optimization algorithm. This paper proposes a subpixel mapping method based on modified binary quantum particle swarm optimization (MBQPSO) to solve the above issues. The initial subpixel mapping imagery is obtained according to spectral unmixing results. We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the objective function, which is formulated based on different connected regional perimeter calculating methods. To reduce time complexity, a target optimization strategy of global iteration combined with local iteration is performed. The MBQPSO is tested on standard test functions and results show that MBQPSO has the best performance on global optimization and convergent rate. Then, we analyze the proposed algorithm qualitatively and quantitatively by simulated and real experiment; results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency. |
| format | Article |
| id | doaj-art-60608965326a45a4a047b14e5746c289 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-60608965326a45a4a047b14e5746c2892025-02-03T01:07:58ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552017-01-01201710.1155/2017/26832482683248Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm OptimizationShuhan Chen0Xiaorun Li1Liaoying Zhao2College of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaInstitute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSubpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature and local convergence of the optimization algorithm. This paper proposes a subpixel mapping method based on modified binary quantum particle swarm optimization (MBQPSO) to solve the above issues. The initial subpixel mapping imagery is obtained according to spectral unmixing results. We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the objective function, which is formulated based on different connected regional perimeter calculating methods. To reduce time complexity, a target optimization strategy of global iteration combined with local iteration is performed. The MBQPSO is tested on standard test functions and results show that MBQPSO has the best performance on global optimization and convergent rate. Then, we analyze the proposed algorithm qualitatively and quantitatively by simulated and real experiment; results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency.http://dx.doi.org/10.1155/2017/2683248 |
| spellingShingle | Shuhan Chen Xiaorun Li Liaoying Zhao Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization Journal of Electrical and Computer Engineering |
| title | Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization |
| title_full | Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization |
| title_fullStr | Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization |
| title_full_unstemmed | Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization |
| title_short | Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization |
| title_sort | subpixel mapping method of hyperspectral images based on modified binary quantum particle swarm optimization |
| url | http://dx.doi.org/10.1155/2017/2683248 |
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