Change Detection Method for Wavelength- Resolution SAR Images Based on Bayes' Theorem: An Iterative Approach

This paper presents an iterative change detection (CD) method based on Bayes' theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods.