2007 IEEE International Conference on Image Processing - San Antonio, Texas, U.S.A. - September 16-19, 2007

Technical Program

Paper Detail

Paper:TP-L4.2
Session:Geosciences and Remote Sensing I
Time:Tuesday, September 18, 14:50 - 15:10
Presentation: Lecture
Title: FAST HYPERSPECTRAL ANOMALY DETECTION VIA SVDD
Authors: Amit Banerjee; The Johns Hopkins University 
 Philippe Burlina; The Johns Hopkins University 
 Reuven Meth; SET Corporation 
Abstract: We present a method for fast anomaly detection in hyperspectral imagery (HSI) based on the Support Vector Data Description (SVDD) algorithm. The SVDD is a single class, non-parametric approach for modeling the support of a distribution. A global SVDD anomaly detector is developed that utilizes the SVDD to model the distribution of the spectra of pixels randomly selected from the entire image. Experiments on Wide Area Airborne Mine Detection (WAAMD) hyperspectral data show improved Receiver Operating Characteristic (ROC) detection performance when compared to the local SVDD detector and other standard anomaly detectors (including RX and GMRF). Furthermore, one-second processing time using desktop computers on several 256x256x145 datacubes is achieved.



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