Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation
In recent years, some algorithms based on sparse representation have been proposed to improve the detection performance for hyperspectral anomaly detection. Among these algorithms, the background joint sparse representation (BJSR) algorithm adaptively selects the most representative background bases...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-01-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/22797254.2017.1331697 |
| _version_ | 1819101979645837312 |
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| author | Lili Zhang Chunhui Zhao |
| author_facet | Lili Zhang Chunhui Zhao |
| author_sort | Lili Zhang |
| collection | DOAJ |
| description | In recent years, some algorithms based on sparse representation have been proposed to improve the detection performance for hyperspectral anomaly detection. Among these algorithms, the background joint sparse representation (BJSR) algorithm adaptively selects the most representative background bases for the local region and can obtain satisfactory results. However, BJSR mainly considers spectral characteristics of hyperspectral image. In this paper, we propose a BJSR-based spectral–spatial method. BJSR is first employed to process the original hyperspectral image in spectral domain. Then, linear local tangent space alignment (LLTSA) is used to obtain the low-dimensional manifold of the hyperspectral image. Next, spatial BJSR is used to process the low-dimensional manifold obtained by LLTSA. Finally, the proposed algorithm combines spectral BJSR with spatial BJSR to detect the anomaly targets. The experimental results demonstrate that the proposed algorithm can achieve a better performance when compared with the comparison algorithms. |
| first_indexed | 2024-12-22T01:27:16Z |
| format | Article |
| id | doaj.art-bc0482c205d14d97af6897904b5423bf |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-12-22T01:27:16Z |
| publishDate | 2017-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-bc0482c205d14d97af6897904b5423bf2022-12-21T18:43:35ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542017-01-0150136237610.1080/22797254.2017.13316971331697Hyperspectral anomaly detection based on spectral–spatial background joint sparse representationLili Zhang0Chunhui Zhao1Harbin Engineering University, College of Information and Communication EngineeringHarbin Engineering University, College of Information and Communication EngineeringIn recent years, some algorithms based on sparse representation have been proposed to improve the detection performance for hyperspectral anomaly detection. Among these algorithms, the background joint sparse representation (BJSR) algorithm adaptively selects the most representative background bases for the local region and can obtain satisfactory results. However, BJSR mainly considers spectral characteristics of hyperspectral image. In this paper, we propose a BJSR-based spectral–spatial method. BJSR is first employed to process the original hyperspectral image in spectral domain. Then, linear local tangent space alignment (LLTSA) is used to obtain the low-dimensional manifold of the hyperspectral image. Next, spatial BJSR is used to process the low-dimensional manifold obtained by LLTSA. Finally, the proposed algorithm combines spectral BJSR with spatial BJSR to detect the anomaly targets. The experimental results demonstrate that the proposed algorithm can achieve a better performance when compared with the comparison algorithms.http://dx.doi.org/10.1080/22797254.2017.1331697Hyperspectral imageanomaly detectionspectral–spatial methodbackground joint sparse representationlinear local tangent space alignmentalignment matrix |
| spellingShingle | Lili Zhang Chunhui Zhao Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation European Journal of Remote Sensing Hyperspectral image anomaly detection spectral–spatial method background joint sparse representation linear local tangent space alignment alignment matrix |
| title | Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation |
| title_full | Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation |
| title_fullStr | Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation |
| title_full_unstemmed | Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation |
| title_short | Hyperspectral anomaly detection based on spectral–spatial background joint sparse representation |
| title_sort | hyperspectral anomaly detection based on spectral spatial background joint sparse representation |
| topic | Hyperspectral image anomaly detection spectral–spatial method background joint sparse representation linear local tangent space alignment alignment matrix |
| url | http://dx.doi.org/10.1080/22797254.2017.1331697 |
| work_keys_str_mv | AT lilizhang hyperspectralanomalydetectionbasedonspectralspatialbackgroundjointsparserepresentation AT chunhuizhao hyperspectralanomalydetectionbasedonspectralspatialbackgroundjointsparserepresentation |