@inproceedings{f877b21dba7f460d8f793d570b6b78c2,
title = "Background characterization for subpixel target detection",
abstract = "When performing point target detection in hyperspectral imagery, one often uses the spectral inverse covariance matrix to whiten the natural noise of the image. Since the cube is not necessarily stationary, we wish to understand when segmentation is worthwhile to provide different covariance matrices for different areas of the cube. Using simulations and several new analytical tools, we propose general guidelines for when segmentation is useful.",
keywords = "Hyperspectral, Segmentation, Target Detection",
author = "Stanley Rotman and Sapir Ben-Yakar and Dan Blumberg",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "https://doi.org/10.1109/IGARSS.2017.8127210",
language = "American English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
pages = "1344--1346",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
}