A variational change detection method for multitemporal SAR images

In this letter, we develop a variational model for change detection in multitemporal synthetic aperture radar (SAR) images. SAR images are typically polluted by multiplicative noise, therefore ordinary active contour model (ACM), or the snake model, for image segmentation is not suitable for change detection in multitemporal SAR images. Our model is a generalization of ACM under the assumption that the image data fits the Generalized Gaussian Mixture (GGM) model. Our method first computes the log-ratio image of the input multitemporal SAR images. Then the method iteratively executes the following two steps until convergence: (1) estimate the parameters for the generalized Gaussian distributions inside and outside the current evolving curve using maximum-likelihood estimation; (2) evolve the current curve according to the image data and the parameters previously estimated. When convergence is achieved, the location of the evolving curve depicts the changed and the unchanged areas. Experiments were carried out on both semi-simulated data set and real data set. Results showed that the proposed method achieves total error rates of 0.43% and 1.05%, for semi-simulated and real data sets, respectively, which were comparable to other prevalent methods.