@inproceedings{3699b679c506451aa72f3aea36ffd873,
title = "Using improved outlier estimation for hyperspectral target detection",
abstract = "We present a thorough examination of different noise estimation methods for usage with target detection algorithms for hyperspectral datasets. The different algorithms were designed with two approaches for dealing with outliers: The first allows outliers to contribute beyond their actual population to the estimated distribution, while the second approach limits them. In Addition, the matched filter distribution on the eigen-direction was analyzed using PCA for each algorithm, presenting a novel way to compare and examine the behavior of target detection method.",
keywords = "Covariance estimation, Hyperspectral target detection, Matched filter, Outlier estimation",
author = "Sagiv Dvash and Stanley Rotman",
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.2016.7806194",
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",
}