Unsupervised Change Detection From Multichannel SAR Data by Markovian Data Fusion

In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared with single-channel SAR. However, exploiting this potential requires accurate and automatic techniques to generate change maps from (multichannel) SAR images acquired over the same geographic region in different polarizations or at different frequencies at different times. In this paper, a contextual unsupervised change-detection technique (based on a data-fusion approach) is proposed for two-date multichannel SAR images. Each SAR channel is modeled as a distinct information source, and a Markovian approach to data fusion is adopted. A Markov random field model is introduced that combines together the information conveyed by each SAR channel and the spatial contextual information concerning the correlation among neighboring pixels and formulated by using "energy functions." In order to address the task of the estimation of the model parameters, the expectation-maximization algorithm is combined with the recently proposed "method of log-cumulants." The proposed technique was experimentally validated with semisimulated multipolarization and multifrequency data and with real SIR-C/XSAR images.