Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs
Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of s...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/9998185 |
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| author | Eryang Chen Ruichun Chang Kaibo Shi Ansheng Ye Fang Miao Jianghong Yuan Ke Guo Youhua Wei Yiping Li |
| author_facet | Eryang Chen Ruichun Chang Kaibo Shi Ansheng Ye Fang Miao Jianghong Yuan Ke Guo Youhua Wei Yiping Li |
| author_sort | Eryang Chen |
| collection | DOAJ |
| description | Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method. |
| format | Article |
| id | doaj-art-a589cb8316b343769fa79b33ba355acf |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-a589cb8316b343769fa79b33ba355acf2025-02-03T01:25:10ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/99981859998185Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random MultigraphsEryang Chen0Ruichun Chang1Kaibo Shi2Ansheng Ye3Fang Miao4Jianghong Yuan5Ke Guo6Youhua Wei7Yiping Li8College of Geophysics, Chengdu University of Technology, Chengdu 610059, ChinaGeomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, ChinaSchool of Electronic Information and Eletical Engineering, Chengdu University, Chengdu 610106, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu 610059, ChinaKey Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan, Chengdu University, Chengdu 610106, ChinaSchool of Intelligent Engineering, Sichuan Changjiang Vocational College, Chengdu 610106, ChinaGeomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, ChinaGeomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Geophysics, Chengdu University of Technology, Chengdu 610059, ChinaHyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.http://dx.doi.org/10.1155/2021/9998185 |
| spellingShingle | Eryang Chen Ruichun Chang Kaibo Shi Ansheng Ye Fang Miao Jianghong Yuan Ke Guo Youhua Wei Yiping Li Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs Discrete Dynamics in Nature and Society |
| title | Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs |
| title_full | Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs |
| title_fullStr | Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs |
| title_full_unstemmed | Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs |
| title_short | Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs |
| title_sort | spectral spatial hyperspectral image semisupervised classification by fusing maximum noise fraction and adaptive random multigraphs |
| url | http://dx.doi.org/10.1155/2021/9998185 |
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