In this work we propose a novel unsupervised algorithm for designing multispectral filters that are tuned for local anomaly detection algorithms. This problem is formulated as a problem of channel reduction in hyperspectral images, whichisperformedbyreplacingsubsetsofadjacentspectral bands by their means. An optimal partition of hyperspectralbandsisobtainedbyminimizingtheMaximumofMahalanobis Norms (MXMN) of errors, obtained due to misrepresentation of hyperspectral bands by constants. By minimizing the MXMN of errors, one reduces the anomaly contributiontotheerrors,whichallowstoretainmoreanomalyrelatedinformationinthereducedchannels. Wedemonstrate thattheproposedalgorithmproducesbetterresults,interms of the Receiver Operation Characteristic (ROC) curve of a benchmarkanomalydetectionalgorithm(RX)-appliedafter the dimensionality reduction, as compared to two other dimensionalityreductiontechniques,includingPrincipalComponent Analysis (PCA). 1
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.