@inproceedings{6e092a2574ab44919ef3c546b0dc37f5,
title = "Hyperspectral data cube segmentation analysis in sub-pixel target detection",
abstract = "Segmentation is useful during sub-pixel target detection in hyperspectral data. Previous works have showed that improving performance in sub-pixel target detection can be achieved by making better estimates of the covariance matrix by using segmentation. One of the challenges mentioned was that pixel assignment has been influenced by the target, and therefore a reassignment after segmentation was needed. We examined and compared several methods to deal with this challenge before the segmentation process, as well as to check if this was essential for our algorithm{\textquoteright}s success. Using simulations and several analytical tools we analyzed the matched-filter algorithm, both with and without segmentation, and compare performances of the receiver operating characteristic curves. We found there is no need to perform reassignment after segmentation; segmentation is effective even with the presence of the target in the examined pixel.",
keywords = "Covariance matrix, Hyperspectral, Matched Filter, ROC curve, Segmentation, Subpixel Target Detection",
author = "Avraham, {Eliya Ben} 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 = "10.1117/12.2585067",
language = "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",
}