Name
Title | ||
|---|---|---|
Comparative Analysis of Covariance Matrix Estimation for Anomaly Detection in Hyperspectral Images |
Abstract | ||
|---|---|---|
Covariance matrix estimation is fundamental for anomaly detection, especially for the Reed and Xiaoli Yu (RX) detector. Anomaly detection is challenging in hyperspectral images because the data has a high correlation among dimensions, heavy tailed distributions and multiple clusters. This paper comparatively evaluates modern techniques of covariance matrix estimation based on the performance and volume the RX detector. To address the different challenges, experiments were designed to systematically examine the robustness and effectiveness of various estimation techniques. In the experiments, three factors were considered, namely, sample size, outlier size, and modification in the distribution of the sample. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JSTSP.2015.2442213 | Selected Topics in Signal Processing, IEEE Journal of |
Keywords | Field | DocType |
correlation,detectors,robustness,maximum likelihood estimation,covariance estimation | Anomaly detection,Estimation of covariance matrices,Pattern recognition,Computer science,Outlier,Covariance intersection,Hyperspectral imaging,Artificial intelligence,Covariance matrix,Detector,Analysis of covariance | Journal |
Volume | Issue | ISSN |
PP | 99 | 1932-4553 |
Citations | PageRank | References |
3 | 0.38 | 34 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Santiago Velasco-Forero | 1 | 178 | 24.20 |
| Marcus Chen | 2 | 3 | 0.38 |
| Alvina Goh | 3 | 3 | 0.38 |
| Sze Kim Pang | 4 | 3 | 0.38 |