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A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection

Research article (IEEE Transactions on Geoscience and Remote Sensing, 2015) · cited 466× · AI/ML
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A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection

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A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-low-rank-and-sparse-matrix-decomposition-based-mahalanobis-distance-method-for-hyperspectral-anomaly-detection
MLA “A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-low-rank-and-sparse-matrix-decomposition-based-mahalanobis-distance-method-for-hyperspectral-anomaly-detection.
BibTeX @misc{4ortxyz_a-low-rank-and-sparse-matrix-decomposition-based-mahalanobis-distance-method-for-hyperspectral-anomaly-detection_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection}}, year = {2026}, url = {https://4ort.xyz/entity/a-low-rank-and-sparse-matrix-decomposition-based-mahalanobis-distance-method-for-hyperspectral-anomaly-detection}, note = {Accessed: 2026-05-24}}
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