@inproceedings{5b859e61c37c4b4d9d9f76e1927c8f48,
title = "Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections",
abstract = "In image processing, the Matched Filter algorithm uses the estimated covariance matrix to give each pixel a score based on the similarity between the pixel and the signature of the target. While using this target detection algorithm, false alarms are inevitable. In order to solve this problem, a method using an iterative process to produce a second covariance matrix which only uses the most likely false alarms was presented [6]. In this paper, we test this method, attempt to improve it, and expand on the cases in which it is the most effective. In all cases, the new method showed a decrease in false alarms, and in some cases a decrease of over 85%.",
author = "Shabat, {Idan Ben} and Lihi Zinger and Rotman, {Stanley R.}",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII 2021 ; Conference date: 12-04-2021 Through 16-04-2021",
year = "2021",
month = jan,
day = "1",
doi = "https://doi.org/10.1117/12.2585168",
language = "American English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Miguel Velez-Reyes and Messinger, {David W.}",
booktitle = "Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII",
address = "United States",
}