Wavelet-based markov models for clutter characterization in IR and SAR images

This paper presents wavelet-based methods for characterizing clutter in infrared (IR) and synthetic aperture radar (SAR) images. With our methods, the operating parameters of automatic target recognition (ATR) systems can automatically adapt to local clutter conditions. Structured clutter, which can confuse ATR systems, possesses correlation across scale in the wavelet domain. We model this correlation using wavelet-domain hidden Markov trees, for which efficient parameter estimation algorithms exist. Based on these models, we develop analytical methods for estimating the false alarm rates of mean-squared-error classifiers. These methods are equally useful for determining threshold levels for constant false alarm rate (CFAR) detectors.