Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation
Most of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/11/1318 |
| _version_ | 1818975030017523712 |
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| author | Kun Tan Zengfu Hou Fuyu Wu Qian Du Yu Chen |
| author_facet | Kun Tan Zengfu Hou Fuyu Wu Qian Du Yu Chen |
| author_sort | Kun Tan |
| collection | DOAJ |
| description | Most of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In order to obtain various local spatial distributions with the neighboring pixels of the pixels under test, the LSAD algorithm exploits a multiple-window sliding filter, which can be computationally expensive and time-consuming. In this paper, to address these issues, two modified LSAD-based methods are proposed. The first method, called local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD), is based on the concept that each pixel in the background can be approximately represented by its spatial neighborhood. The second method, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW), uses the surrounding pixels collected inside the outer window, while outside the inner window, to linearly represent the test pixel and introduces collaborative representation and inverse distance weight to further improve the computational speed and detection precision, respectively. The proposed methods were applied to a synthetic dataset and three real datasets. The experimental results show that the proposed methods have a better detection accuracy and computational speed when compared with the LSAD algorithm and others. |
| first_indexed | 2024-12-20T15:49:28Z |
| format | Article |
| id | doaj.art-58f6cd65e73946f49990fbca7d2e1c74 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T15:49:28Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-58f6cd65e73946f49990fbca7d2e1c742022-12-21T19:34:44ZengMDPI AGRemote Sensing2072-42922019-06-011111131810.3390/rs11111318rs11111318Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative RepresentationKun Tan0Zengfu Hou1Fuyu Wu2Qian Du3Yu Chen4Key Laboratory of Geographic Information (Ministry of Education), East China Normal University, Shanghai 200241, ChinaKey Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, ChinaDepartment of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USAKey Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, ChinaMost of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In order to obtain various local spatial distributions with the neighboring pixels of the pixels under test, the LSAD algorithm exploits a multiple-window sliding filter, which can be computationally expensive and time-consuming. In this paper, to address these issues, two modified LSAD-based methods are proposed. The first method, called local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD), is based on the concept that each pixel in the background can be approximately represented by its spatial neighborhood. The second method, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW), uses the surrounding pixels collected inside the outer window, while outside the inner window, to linearly represent the test pixel and introduces collaborative representation and inverse distance weight to further improve the computational speed and detection precision, respectively. The proposed methods were applied to a synthetic dataset and three real datasets. The experimental results show that the proposed methods have a better detection accuracy and computational speed when compared with the LSAD algorithm and others.https://www.mdpi.com/2072-4292/11/11/1318anomaly detectionhyperspectral imagerycollaborative representationunsupervised nearest regularized subspacelocal summation |
| spellingShingle | Kun Tan Zengfu Hou Fuyu Wu Qian Du Yu Chen Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation Remote Sensing anomaly detection hyperspectral imagery collaborative representation unsupervised nearest regularized subspace local summation |
| title | Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation |
| title_full | Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation |
| title_fullStr | Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation |
| title_full_unstemmed | Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation |
| title_short | Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation |
| title_sort | anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation |
| topic | anomaly detection hyperspectral imagery collaborative representation unsupervised nearest regularized subspace local summation |
| url | https://www.mdpi.com/2072-4292/11/11/1318 |
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