ATR performance of a Rician model for SAR images

Radar targets often have both specular and diffuse scatterers. A conditionally Rician model for the amplitudes of pixels in Synthetic Aperture Radar (SAR) images quantitatively accounts for both types of scatterers. Conditionally Rician models generalize conditionally Gaussian models by including means with uniformly distributed phases in the complex imagery. Qualitatively, the values of the two parameters in the Rician model bring out different aspects of the images. For automatic target recognition (ATR), log-likelihoods are computed using parameters estimated from training data. Using MSTAR data, the resulting performance for a number of four class ATR problems representing both standard and extended operating conditions is studied and compared to the performance of corresponding conditionally Gaussian models. Performance is measured quantitatively using the Hilbert-Schmidt squared error for orientation estimation and the probability of error for recognition. For the MSTAR dataset used, the results indicate that algorithms based on conditionally Rician and conditionally Gaussian models yield similar results when a rich set of training data is available, but the performance under the Rician model suffers with smaller training sets. Due to the smaller number of distribution parameters, the conditionally Gaussian approach is able to yield a better performance for any fixed complexity.