@inproceedings{c923302ccbba4642a3e08fa0db186c7a,
title = "Stationary covariance matrices for hyperspectral point target detection",
abstract = "Segmentation appears to be an attractive preprocessing procedure when performing point target detection in hyperspectral data. Unfortunately, the computation cost is not always worth the improvement. Recent work proposed guidelines to decide if segmentation is worthwhile, but the results were only known after segmentation was performed. In this paper, we study the statistical basis of the covariance matrix used in hyperspectral subpixel target detection and propose a metric applied directly to the original matrix. Based on the degree of non-stationarity of the original matrix, one can predict how worthwhile it is to compute segmentation before analyzing on the image.",
keywords = "Hyperspectral, Segmentation, Stationarity, Sub-Pixel Target Detection",
author = "Yoram Furth and Adi Falik and Rotman, {Stanley R.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/IGARSS.2018.8518063",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "4245--4248",
booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",
address = "United States",
}