Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning
Hyperspectral imagery (HSI) classification, which attempts to assign hyperspectral pixels with proper labels, has drawn significant attention in various applications. Recently, the graph-based semisupervised learning (SSL) approaches have shown the outstanding ability to handle the situation of limi...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9645331/ |
| _version_ | 1828058254073135104 |
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| author | Yanling Miao Mulin Chen Yuan Yuan Jocelyn Chanussot Qi Wang |
| author_facet | Yanling Miao Mulin Chen Yuan Yuan Jocelyn Chanussot Qi Wang |
| author_sort | Yanling Miao |
| collection | DOAJ |
| description | Hyperspectral imagery (HSI) classification, which attempts to assign hyperspectral pixels with proper labels, has drawn significant attention in various applications. Recently, the graph-based semisupervised learning (SSL) approaches have shown the outstanding ability to handle the situation of limited labeled data for classification task. However, it is hard to construct the pairwise adjacent graph due to the high dimensionality of hyperspectral data. Besides, the graph-based SSL models are usually decided by a single classifier, which fail to effectively learn the complex structures and intrinsic properties of HSI. To address these problems, we propose a novel graph-based SSL classification model for HSI, which is based on random multigraphs construction and ensemble strategy (RMGE). Specifically, the anchor graph (AG) is constructed with spatial–spectral features, which integrates the spatial characteristics extracted by local binary pattern on each selected spectrum, preserving fine structures of local region. In order to enhance the discriminative capability of the classifier and avoid the trivial solution, the maximum entropy regularization is added into adjacent AG model. In addition, to capture the diversity of HSI data effectively, we design the ensemble framework by employing multiple AGs to learn HSI features. Experiments conducted on real hyperspectral datasets indicate that the proposed RMGE shows better performance than that of state-of-the-art approaches. |
| first_indexed | 2024-04-10T21:25:29Z |
| format | Article |
| id | doaj.art-d41b5e37edb248f6b1e18258689b9f14 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-10T21:25:29Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-d41b5e37edb248f6b1e18258689b9f142023-01-20T00:00:16ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-011564165310.1109/JSTARS.2021.31329939645331Hyperspectral Imagery Classification via Random Multigraphs Ensemble LearningYanling Miao0https://orcid.org/0000-0001-6037-5726Mulin Chen1https://orcid.org/0000-0003-4634-3802Yuan Yuan2Jocelyn Chanussot3https://orcid.org/0000-0003-4817-2875Qi Wang4https://orcid.org/0000-0002-7028-4956School of Computer Science and the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, ChinaSchool of Artificial Intelligence Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, ChinaSchool of Artificial Intelligence Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, ChinaUniv. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, Grenoble, FranceSchool of Artificial Intelligence Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, ChinaHyperspectral imagery (HSI) classification, which attempts to assign hyperspectral pixels with proper labels, has drawn significant attention in various applications. Recently, the graph-based semisupervised learning (SSL) approaches have shown the outstanding ability to handle the situation of limited labeled data for classification task. However, it is hard to construct the pairwise adjacent graph due to the high dimensionality of hyperspectral data. Besides, the graph-based SSL models are usually decided by a single classifier, which fail to effectively learn the complex structures and intrinsic properties of HSI. To address these problems, we propose a novel graph-based SSL classification model for HSI, which is based on random multigraphs construction and ensemble strategy (RMGE). Specifically, the anchor graph (AG) is constructed with spatial–spectral features, which integrates the spatial characteristics extracted by local binary pattern on each selected spectrum, preserving fine structures of local region. In order to enhance the discriminative capability of the classifier and avoid the trivial solution, the maximum entropy regularization is added into adjacent AG model. In addition, to capture the diversity of HSI data effectively, we design the ensemble framework by employing multiple AGs to learn HSI features. Experiments conducted on real hyperspectral datasets indicate that the proposed RMGE shows better performance than that of state-of-the-art approaches.https://ieeexplore.ieee.org/document/9645331/Ensemble learninggraph-based semisupervised learning (SSL)hypersectral imagerymaximum entropy regularization |
| spellingShingle | Yanling Miao Mulin Chen Yuan Yuan Jocelyn Chanussot Qi Wang Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Ensemble learning graph-based semisupervised learning (SSL) hypersectral imagery maximum entropy regularization |
| title | Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning |
| title_full | Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning |
| title_fullStr | Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning |
| title_full_unstemmed | Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning |
| title_short | Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning |
| title_sort | hyperspectral imagery classification via random multigraphs ensemble learning |
| topic | Ensemble learning graph-based semisupervised learning (SSL) hypersectral imagery maximum entropy regularization |
| url | https://ieeexplore.ieee.org/document/9645331/ |
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