Change detection in multi-temporal TerraSAR-X SAR images using a hierarchical Markov model on regions

This paper addresses the problem of change detection in high-resolution multi-temporal synthetic aperture radar (SAR) images (e.g. TerraSAR-X SAR images). Given two images, the proposed method first computes a difference map between them, by taking into account both the spatial and temporal correlations. Change detection is then formulated as a binary (changed/unchanged) segmentation problem of the difference map. A hierarchical Markov model (HMM) is defined on the multi-scale over-segmented regions of the difference map. The change map is finally inferred by relying on the hierarchical marginal posterior mode (HMPM) of the HMM. Experimental results on multi-temporal TerraSAR-X SAR images demonstrate the effectiveness and the reliability of the proposed approach.