Unsupervised multiscale change detection in multitemporal synthetic aperture radar images

An unsupervised change detection technique for synthetic aperture radar (SAR) images is proposed by conducting probabilistic Bayesian inferencing with expectation maximization-based parameter estimation to perform unsupervised thresholding over the data collected from the dual-tree complex wavelet transform (DT-CWT) subbands generated at the various scales. The proposed approach exploits a DT-CWT-based multiscale decomposition of the log-ratio image aimed at achieving different scales of representation of the change signal. Experimental results obtained on multitemporal SAR images acquired by the ERS-1, and JERS satellites confirm the effectiveness of the proposed approach.