Name
Abstract | ||
|---|---|---|
Anomaly Detection methods are used when there is not enough information about the target to detect. These methods search for pixels in the image with spectral characteristics that differ from the background. The most widespread detection test, the RX-detector, is based on the Mahalanobis distance and on the background statistical characterization through the mean vector and the covariance matrix. Although non-Gaussian distributions have already been introduced for background modeling in Hyperspectral Imaging, the parameters estimation is still performed using the Maximum Likelihood Estimates for Gaussian distribution. This paper describes robust estimation procedures more suitable for non-Gaussian environment. Therefore, they can be used as plug-in estimators for the RX-detector leading to some great improvement in the detection process. This theoretical improvement has been evidenced over two real hyperspectral images. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6947518 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
covariance matrices,geophysical image processing,geophysical techniques,hyperspectral imaging,object detection,parameter estimation,spectral analysis,statistical analysis,statistical distributions,Mahalanobis distance,RX-detector,background modeling,background statistical characterization,covariance matrix,detection test,hyperspectral imaging,image pixels,mean vector,nonGaussian distribution,parameters estimation,plug-in estimators,robust anomaly detection method,spectral characteristics,target detection,M-estimators,anomaly detection,elliptical distributions,hypespectral imaging | Anomaly detection,Computer vision,Estimation of covariance matrices,Pattern recognition,Computer science,Mahalanobis distance,Hyperspectral imaging,Gaussian,Artificial intelligence,Pixel,Covariance matrix,Estimator | Conference |
ISSN | Citations | PageRank |
2153-6996 | 3 | 0.45 |
References | Authors | |
5 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joana Frontera-Pons | 1 | 25 | 4.07 |
| Miguel Angel Veganzones | 2 | 122 | 10.58 |
| Santiago Velasco-Forero | 3 | 178 | 24.20 |
| Frédéric Pascal | 4 | 128 | 16.30 |
| Jean Philippe Ovarlez | 5 | 190 | 25.11 |
| Jocelyn Chanussot | 6 | 4145 | 272.11 |
| Frontera-Pons, J. | 7 | 3 | 0.45 |
| Velasco-Forero, S. | 8 | 3 | 0.45 |