Beta‐divergence based two‐phase segmentation model for synthetic aperture radar images. Issue 20 (9th September 2016) - Woo, H.
Abstract : A general two‐phase segmentation model (beta‐CCV) for synthetic aperture radar (SAR) images is introduced. The proposed model is based on the beta‐divergence similarity measure and the convex‐relaxed Chan–Vese model. The main advantage of beta‐divergence with non‐positive value of beta is that it is an expedient similarity measure for high dynamic range SAR images with strong scatterers. In addition, due to the global optimum property of beta‐divergence, naturally merge the beta‐divergence similarity measure into the convex‐relaxed Chan–Vese model. Hence, we can find solutions of beta‐CCV in a more stable way through convex optimisation. The proposed beta‐CCV model shows overall better performance than the state‐of‐the‐art segmentation method.