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Clutter and anomaly removal for enhanced target detection

In this paper we investigate the use of anomaly detection to identify pixels to be removed prior to covariance computation. The resulting covariance matrix provides a better model of the image background and is less likely to be tainted by target spectra. In our tests, this method results in robust improvement in target detection performance for quadratic detection algorithms. Tests are conducted using imagery and targets freely available online. The imagery was acquired over Cooke City, Montana, a small town near Yellowstone Park, using the HyMap V/NIR/SWIR sensor with 126 spectral bands. There are three vehicle and four fabric targets located in the town and surrounding area.