@inproceedings{c12ba7b4db754f52848b1487b82d7612,
title = "Analysis of hyperspectral anomaly change detection algorithms",
abstract = "In this paper, we test anomaly change detection algorithms in hyperspectral images. Focusing on difference-based algorithms, our goal is to optimize performance using new methods that utilize the spatial and statistical characteristics of the images. These methods increase the probability of detection while minimizing false alarms. The algorithms are tested on the hyperspectral images of the Rochester Institute of Technology (RIT).",
keywords = "Anomaly detection, Change detection, Hyperspectral",
author = "Yair Elhadad and Rotman, \{Stanley R.\} and Dan Blumberg",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016 ; Conference date: 21-08-2016 Through 24-08-2016",
year = "2016",
month = jun,
day = "28",
doi = "10.1109/WHISPERS.2016.8071746",
language = "American English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
booktitle = "2016 8th Workshop on Hyperspectral Image and Signal Processing",
}