A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm
Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge fo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/7/1293 |
| _version_ | 1797433153028096000 |
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| author | Zhiwei Ye Wenhui Cai Shiqin Liu Kainan Liu Mingwei Wang Wen Zhou |
| author_facet | Zhiwei Ye Wenhui Cai Shiqin Liu Kainan Liu Mingwei Wang Wen Zhou |
| author_sort | Zhiwei Ye |
| collection | DOAJ |
| description | Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper. |
| first_indexed | 2024-03-09T10:12:04Z |
| format | Article |
| id | doaj.art-ff27c74cd9334e2d869e019a629ad92b |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T10:12:04Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-ff27c74cd9334e2d869e019a629ad92b2023-12-01T22:44:19ZengMDPI AGSymmetry2073-89942022-06-01147129310.3390/sym14071293A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization AlgorithmZhiwei Ye0Wenhui Cai1Shiqin Liu2Kainan Liu3Mingwei Wang4Wen Zhou5School of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaHigh Performance Computing Academician Workstation of Sanya University, Sanya 572022, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaHyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper.https://www.mdpi.com/2073-8994/14/7/1293hyperspectral imageband selectionhybrid rice optimization algorithmopposition-based learningdifferential evolution |
| spellingShingle | Zhiwei Ye Wenhui Cai Shiqin Liu Kainan Liu Mingwei Wang Wen Zhou A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm Symmetry hyperspectral image band selection hybrid rice optimization algorithm opposition-based learning differential evolution |
| title | A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm |
| title_full | A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm |
| title_fullStr | A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm |
| title_full_unstemmed | A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm |
| title_short | A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm |
| title_sort | band selection approach for hyperspectral image based on a modified hybrid rice optimization algorithm |
| topic | hyperspectral image band selection hybrid rice optimization algorithm opposition-based learning differential evolution |
| url | https://www.mdpi.com/2073-8994/14/7/1293 |
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