An EM-MPM approach to unsupervised change detection in multitemporal SAR images

Abstract

In this paper, we propose an unsupervised change-detection method which considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images. A Markov Random Filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of independency of pixels each other and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on a MRFs model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an Expectation-maximum (EM) algorithm for parameter estimation in completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.