Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation
In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/7/1253 |
| _version_ | 1797540034049474560 |
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| author | Guichi Liu Lei Gao Lin Qi |
| author_facet | Guichi Liu Lei Gao Lin Qi |
| author_sort | Guichi Liu |
| collection | DOAJ |
| description | In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms. |
| first_indexed | 2024-03-10T12:54:19Z |
| format | Article |
| id | doaj.art-8504e00731cc4f1682cf42783c2f027a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:54:19Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8504e00731cc4f1682cf42783c2f027a2023-11-21T12:02:35ZengMDPI AGRemote Sensing2072-42922021-03-01137125310.3390/rs13071253Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive RepresentationGuichi Liu0Lei Gao1Lin Qi2School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, ChinaDepartment of Electrical and Computer Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaSchool of Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaIn recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms.https://www.mdpi.com/2072-4292/13/7/1253hyperspectral classificationdata correlationtrace LassoTikhonov regularization |
| spellingShingle | Guichi Liu Lei Gao Lin Qi Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation Remote Sensing hyperspectral classification data correlation trace Lasso Tikhonov regularization |
| title | Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation |
| title_full | Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation |
| title_fullStr | Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation |
| title_full_unstemmed | Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation |
| title_short | Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation |
| title_sort | hyperspectral image classification via multi feature based correlation adaptive representation |
| topic | hyperspectral classification data correlation trace Lasso Tikhonov regularization |
| url | https://www.mdpi.com/2072-4292/13/7/1253 |
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