@inproceedings{777e82ed55504d3695361ae173bbfa9e,
title = "Effective segmentation for point target detection",
abstract = "Point target detection algorithms in hyperspectral imaging commonly use the spectral inverse covariance matrix to whiten the natural noise of the image. Since the noise in hyperspectral data cubes often suffer from a lack of stationarity, segmentation appears to be an attractive preprocessing operation. However, the literature contains examples of successful and unsuccessful segmentation with no plausible explanation for why some succeed, and others do not. Focusing on one representative algorithm and assuming a target additive model, this paper tracks the underlying causes of when segmentation does improve detection for different target spectra. It then characterizes a real dataset and concludes with ways to improve the detector performance.",
keywords = "Hyperspectral image, point target detection, segmentation, segmented matched filter",
author = "Yoram Furth and Rotman, {Stanley R.}",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX 2023 ; Conference date: 02-05-2023 Through 04-05-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1117/12.2655794",
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 XXIX",
address = "United States",
}