Name
Title | ||
|---|---|---|
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 ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TGRS.2015.2479299 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Covariance matrices,Sparse matrices,Detectors,Hyperspectral imaging,Noise,Approximation methods | Computer vision,Anomaly detection,Pattern recognition,Hyperspectral imaging,Mahalanobis distance,Artificial intelligence,Inverse covariance matrix,Detector,Sparse matrix,Mathematics | Journal |
Volume | Issue | ISSN |
54 | 3 | 0196-2892 |
Citations | PageRank | References |
25 | 0.71 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxiang Zhang | 1 | 167 | 15.28 |
| Bo Du | 2 | 1662 | 130.01 |
| Liangpei Zhang | 3 | 5448 | 307.02 |
| Shugen Wang | 4 | 25 | 1.05 |