@inproceedings{4f620df4d76444c586af923540af0939,
title = "Improved covariance matrix for target detection in hyperspectral imaging",
abstract = "In many image processing applications, the estimation of the covariance matrix is considered an essential step. Estimating the covariance matrix has a great influence on the success or failure of a given algorithm. Usually the covariance matrix is estimated by the sampled covariance matrix of the whole data. The problem with doing so is that anomalies that exist in the data might distort the covariance matrix. This paper presents an approach for covariance matrix estimation that is less prone to anomalies and improves the detection rate. Results on simulations and real life images are presented.",
author = "Ilan Schvartzman and Shimrit Maman and Blumberg, {Dan G.} and Rotman, {Stanley R.}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 ; Conference date: 16-11-2016 Through 18-11-2016",
year = "2017",
month = jan,
day = "4",
doi = "10.1109/ICSEE.2016.7806135",
language = "English",
series = "2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016",
address = "United States",
}