Clutter background spectral density estimation for SAR target recognition with composite correlation filters

Composite correlation filters have been demonstrated in many synthetic aperture radar (SAR) automatic target recognition (ATR) applications because of their ability for class discrimination and distortion-tolerance with shift invariance. For many implementations, a simple model of white noise spectral density has been used to reduce output noise variance. Substituting a colored noise model or real clutter imagery in synthesizing correlation filters has shown improved clutter rejection and target recognition. However, the spectral response of SAR imagery is quite different than other types of images. We demonstrate the use of SAR clutter imagery and a new model for SAR clutter estimation for use in maximum average correlation height (MACH) and optimal tradeoff distance classifier correlation filter (OTDCCF) approaches. Test results using the MSTAR database are shown.