Local and nonlocal structural differences for SAR image change detection

In the synthetic aperture radar (SAR) image change detection problem, the inherent speckle noise significantly degrades the discriminability of changes in the difference image. To address this issue, this paper proposes a local/nonlocal structural differences and Markov random field (MRF) co-segmentation-based method. First, this method constructs fully connected graphs within image patches to capture local structure, and constructs K-nearest neighbour (KNN) graphs among patches to capture the nonlocal structure information of the image. Then, two local and four nonlocal structural difference metrics are computed through graph cross-mapping, enhancing robustness to noise and exploiting spatio-temporal correlations. Finally, the MRF co-segmentation model integrates the change information in different images and the context information in original images for joint segmentation and fusion. The experiments with fourteen comparison methods under three data sets have demonstrated the effectiveness of the proposed method, achieving an average improvement of 6.43% in kappa coefficient.