SAR Image Segmentation with Structure Tensor Based Hierarchical Student’s t-Mixture Model

Synthetic aperture radar (SAR) plays an important role in Satellite IoT, due to its remarkable capability of all-weather monitoring and information acquisition under complicated conditions. It is well-known that SAR image interpretation usually requires accurate segmentation. However, SAR image segmentation inevitably encounters speckle noise because of the unique imaging mechanism of SAR. In order to address the problem, we proposed SAR images segmentation method by combined a hierarchical Student’s t-mixture model (HSMM) with an anisotropic mean template, which can divide the global SAR image segmentation into several sub-clustering-issues efficiently resolved using classical algorithm. With the aid of a non-linear structure tensor for image contents analysis, the adaptive template can explore more spatial correlations between pixels for the purpose of improving HSMM robustness and segmentation accuracy. Experiments results both synthetic and real SAR images demonstrate that our proposed HSMM is more robust to speckle noise and obtains more accurate segmented images.