SAR image segmentation based on level set approach and G⁰A model

This paper proposes an image segmentation method for synthetic aperture radar (SAR), exploring statistical properties of SAR data to characterize image regions. We consider G⁰A distribution parameters for SAR image segmentation, combined to the level set framework. The G⁰A distribution belongs to a class of G distributions that have been successfully used to model different regions in amplitude SAR images for data modeling purpose. Such statistical data model is fundamental to deriving the energy functional to perform region mapping, which is input into our level set propagation numerical scheme that splits SAR images into homogeneous, heterogeneous, and extremely heterogeneous regions. Moreover, we introduce an assessment procedure based on stochastic distance and the G⁰A model to quantify the robustness and accuracy of our approach. Our results demonstrate the accuracy of the algorithms regarding experiments on synthetic and real SAR data.