The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection
Abstract
Anomaly detection is playing an increasingly important role in hyperspectral image (HSI) processing. The traditional anomaly detection methods mainly extract knowledge from the background and use the difference between the anomalies and the background to distinguish them. Anomaly contamination and the inverse covariance matrix problem are the main difficulties with these methods. The low-rank and sparse matrix decomposition (LRaSMD) technique may have the potential to solve the aforementioned hyperspectral anomaly detection problem since it can extract knowledge from both the background and the anomalies. This paper proposes an LRaSMD-based Mahalanobis distance method for hyperspectral anomaly detection (LSMAD). This approach has the following capabilities: 1) takes full advantage of the LRaSMD technique to set the background apart from the anomalies; 2) explores the low-rank prior knowledge of the background to compute the background statistics; and 3) applies the Mahalanobis distance differences to detect the probable anomalies. Extensive experiments were carried out on four HSIs, and it was found that LSMAD shows a better detection performance than the current state-of-the-art hyperspectral anomaly detection methods.
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Citations
Cites
Robust principal component analysis?
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Hyperspectral image data analysis
Landgrebe, D. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974718 · Cited by 1,152
Hyperspectral Subspace Identification
Bioucas-Dias, J. M., & Nascimento, J. M. P. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.918089 · Cited by 1,099
Hyperspectral Image Restoration Using Low-Rank Matrix Recovery
Zhang, H., He, W., Zhang, L., Shen, H., & Yuan, Q. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2284280 · Cited by 887
Self-citationHyperspectral Image Processing for Automatic Target Detection Applications
Manolakis, D. G., Marden, D. B., & Shaw, G. A. (2003) · Cited by 765
Collaborative representation for hyperspectral anomaly detection
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Anomaly detection from hyperspectral imagery
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Cited by
BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection
Wang, D., Zhuang, L., Gao, L., Sun, X., Huang, M., & Plaza, A. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3335484 · Cited by 579
LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection
Li, C., Zhang, B., Hong, D., Yao, J., & Chanussot, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3279834 · Cited by 270
Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection
Cheng, T., & Wang, B. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2936609 · Cited by 266
Hyperspectral anomaly detection with robust graph autoencoders
Fan, G., Ma, Y., Mei, X., Fan, F., Huang, J., & Ma, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3097097 · Cited by 212
Jiang, T., Li, Y., Xie, W., & Du, Q. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2965961 · Cited by 209
Metadata
- Authors
- Zhang, Y., Du, B., Zhang, L., & Wang, S.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2016-03
- Volume
- 54
- Issue
- 3
- Pages
- 1376-1389
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2288752886
- Cited by
- 485
- References
- 36
- FWCI
- 27.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, Y., Du, B., Zhang, L., & Wang, S. (2016). A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2015.2479299
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