Unsupervised Change-Detection from Multi-Channel SAR Data

Synthetic aperture radar (SAR) data presents a great potential for environmental monitoring applications and natural disaster management thanks to their insensitivity to atmospheric and Sun-illumination conditions. However, the automatic generation of change maps from multichannel SAR images acquired on the same geographic area at different times is still an open issue in the remote-sensing literature. In the present paper an automatic unsupervised contextual change-detection method is proposed for two-date multichannel SAR images, by integrating a SAR-specific extension of the Fisher transform with the expectation-maximization (EM) algorithm or with some of its variants (the Landgrebe-Jackson EM and the stochastic EM algorithms), applied according to a Markov random field model for the image data. The method is validated by experiments on SIR-C/XSAR data