 |
Free and accessible knowledge |
Conference papers
Robust anomaly detection in hyperspectral imaging
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.
Cited literature [10 references]
https://hal.archives-ouvertes.fr/hal-01010418
Contributor : Miguel Angel Veganzones Connect in order to contact the contributor
Submitted on : Thursday, June 19, 2014 - 4:40:39 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:09 PM
Long-term archiving on: : Friday, September 19, 2014 - 11:26:44 AM
Contributor : Miguel Angel Veganzones Connect in order to contact the contributor
Submitted on : Thursday, June 19, 2014 - 4:40:39 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:09 PM
Long-term archiving on: : Friday, September 19, 2014 - 11:26:44 AM
File
FronteraIGARSS_14.pdf
Files produced by the author(s)