The use of matched filters on hyperspectral data has made it possible to detect faint signatures. This study uses a modified -means clustering to improve matched filter performance. Several simple bivariate cases are examined in detail, and the interaction of filtering and partitioning is discussed. We show that clustering can reduce within-class variance and group pixels with similar correlation structures. Both of these features improve filter performance. The traditional -means algorithm is modified to work with a sample of the image at each iteration and is tested against two hyperspectral datasets. A new "extreme" centroid initialization technique is introduced and shown to speed convergence. Several matched filtering formulations (the simple matched filter, the clutter matched filter, and the saturated matched filter) are compared for a variety of number of classes and synthetic hyperspectral images. The performance of the various clutter matched filter formulations is similar, all are about an order of magnitude better than the simple matched filter. Clustering is found to improve the performance of all matched filter formulations by a factor of two to five. Clustering in conjunction with clutter matched filtering can improve fifty-fold over the simple case, enabling very weak signals to be detected in hyperspectral images.Index Terms-Clustering, endmember decomposition, gas plumes, hyperspectral, image classification, image partitioning, matched filter, signal detection, spectral mixture analysis, trace element detection. Christopher C. Funk received the B.A. degree with a thesis focused on the content and impact of the scientific revolution in physics in the late renaissance, and the M.A. degree in geography, developing an orographic rainfall model that estimated mountain-induced precipitation rates based on an internal gravity representation of atmospheric winds, both from the University of Chicago, Chicago, IL, in 1989 and 1999, respectively. He worked for several years as a Programmer and Economic Forecaster in Chicago, developing models to predict stock market volatility. In 1996, he joined the University of California, Santa Barbara. His first project there involved building a software package that performed geographically correct (i.e., spherical) interpolation, using a suite of different basis functions. He is an EPA STAR fellow, and his current research focuses primarily on characterizing and predicting patterns of interannual rainfall variability in sub-Saharan Africa.James Theiler received the Ph.D. degree from the Califonia Institute of Technology, Berkeley, in 1987, writing a dissertation on algorithms for identifying chaos in time series.
