@inproceedings{b597d2bfaa054e35acf5a19b5b683413,
title = "Consensus Anomaly Detection Using Clustering Methods in Hyperspectral imagery",
abstract = "A common anomaly detection algorithm for hyperspectral imagery is the RX algorithm based on the Mahalanobis distance of each pixel from the expected value of that pixel. This algorithm can be applied either directly on a hyperspectral image or on a dimensionality-reduced hyperspectral image. Recent work on Non-Negative Matrix Factorization (NNMF) provides a fast-iterative algorithm for decomposing a hyperspectral cube and achieving dimensionality reduction. In this paper, we present the RICHARD (Robust Iterative Consensus Anomaly RX Detection) algorithm that generates more than 100 RX tests after data manipulations (such as Principal Component Analysis (PCA) and NNMF) which vary in their specific parameters; we then use a weighted consensus voting process in order to detect anomalies without any prior knowledge. Using the RICHARD algorithm can enhance our options in finding obscure anomalies which do not appear in every algorithm.",
keywords = "Anomalies detection, Consensus Detection, Hyperspectral, Image Processing, Machine Learning, NNMF algorithm, RX algorithm, SSRX",
author = "Yoav Amiel and Adar Frajman and Rotman, {Stanley R.}",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Imaging Spectrometry XXIV: Applications, Sensors, and Processing 2020 ; Conference date: 24-08-2020 Through 04-09-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.1117/12.2568411",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ientilucci, {Emmett J.} and Pantazis Mouroulis",
booktitle = "Imaging Spectrometry XXIV",
address = "United States",
}