Clustering hyperspectral imagery for improved adaptive matched filter performance

Abstract

This paper offers improvements to adaptive matched filter (AMF) performance by addressing correlation and non-homogeneity problems inherent to hyperspectral imagery (HSI).Theestimation of themeanvector and covariancematrix of the background shouldbe calcu- lated using “target-free” data. This statement reflects the difficulty that including target data in esti- matesofthemeanvectorandcovariancematrixofthebackgroundcouldentail.Thisdatacouldactas statistical outliers and severely contaminate the estimators. This fact serves as the impetus for a 2-stage process: First, attempttoremove the target datafromthe backgroundbywayof theemploy- ment of anomaly detectors. Next, with remaining data being relatively “target-free” the way is cleared for signature matching. Relative to the first stage, we were able to test seven different anomaly detectors, some of which are designed specifically to deal with the spatial correlation ofHSI data and/or the presence of anomalous pixels in local or global meanand covariance estima- tors. Relative to the second stage,we investigated the use of cluster analytic methods to boostAMF performance.Theresearchshowsthataccountingforspatialcorrelationeffects inthedetectoryields nearly“target-free”data for useinanAMFthat is greatly benefitted throughtheuse of cluster analy- sis methods.