Change Detection based on Bayes' Theorem for Intensity Wavelength-Resolution SAR Difference Images

Detecting concealed targets under foliage remains a significant challenge for wavelength-resolution synthetic aperture radar (SAR) systems. This paper proposes the usage of the bivariate Gamma distribution as a clutter model, shown to be a well fitted distribution for intensity SAR difference images, on an noniterative Change Detection (CD) algorithm based on Bayes' Theorem. The results were compared by using ROC curves and a probability of detection of 98.94\% at false alarm ratio of 1 per kilometer squared was achieved, outperforming previously used distributions in the literature.