Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation
The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/12/6943 |
| _version_ | 1797596304468082688 |
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| author | Huan Li Jun Tang Huixin Zhou |
| author_facet | Huan Li Jun Tang Huixin Zhou |
| author_sort | Huan Li |
| collection | DOAJ |
| description | The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on these, this paper proposes a hyperspectral anomaly detection algorithm based on multiple feature joint trilateral filtering and collaborative representation. The algorithm first introduces an improved trilateral filtering algorithm, which utilizes the spatial features of hyperspectral images. The preliminary positions of possible abnormal objects are determined. On this basis, abnormal removal and background filling are performed to obtain a purified background. Finally, the purified background and the original hyperspectral image are used for joint collaborative representation to complete the detection. Experimental results show that the detection accuracy of the algorithm proposed in this paper was efficiently improved by introducing multiple feature joint trilateral filtering, where multiple spatial spectrum features are utilized. |
| first_indexed | 2024-03-11T02:48:41Z |
| format | Article |
| id | doaj.art-5a7d1d86b7804ce9906fa48789da3140 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:48:41Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5a7d1d86b7804ce9906fa48789da31402023-11-18T09:06:43ZengMDPI AGApplied Sciences2076-34172023-06-011312694310.3390/app13126943Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative RepresentationHuan Li0Jun Tang1Huixin Zhou2Department of Physics, Xidian University, No. 2, South Taibai Road, Xi’an 710071, ChinaDepartment of Physics, Xidian University, No. 2, South Taibai Road, Xi’an 710071, ChinaDepartment of Physics, Xidian University, No. 2, South Taibai Road, Xi’an 710071, ChinaThe hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on these, this paper proposes a hyperspectral anomaly detection algorithm based on multiple feature joint trilateral filtering and collaborative representation. The algorithm first introduces an improved trilateral filtering algorithm, which utilizes the spatial features of hyperspectral images. The preliminary positions of possible abnormal objects are determined. On this basis, abnormal removal and background filling are performed to obtain a purified background. Finally, the purified background and the original hyperspectral image are used for joint collaborative representation to complete the detection. Experimental results show that the detection accuracy of the algorithm proposed in this paper was efficiently improved by introducing multiple feature joint trilateral filtering, where multiple spatial spectrum features are utilized.https://www.mdpi.com/2076-3417/13/12/6943hyperspectral anomaly detectionmulti-feature joint trilateral filteringcooperative representationspatial features |
| spellingShingle | Huan Li Jun Tang Huixin Zhou Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation Applied Sciences hyperspectral anomaly detection multi-feature joint trilateral filtering cooperative representation spatial features |
| title | Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation |
| title_full | Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation |
| title_fullStr | Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation |
| title_full_unstemmed | Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation |
| title_short | Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation |
| title_sort | hyperspectral anomaly detection based on multi feature joint trilateral filtering and cooperative representation |
| topic | hyperspectral anomaly detection multi-feature joint trilateral filtering cooperative representation spatial features |
| url | https://www.mdpi.com/2076-3417/13/12/6943 |
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