Fast SAR Image Segmentation Algorithm Based on Global Optimization Method

In order to cope with the non-convexity of energy functional of variational level set segmentation model and its easily getting stuck in local minima, a global optimization problem of the variational level set segmentation model had been studied. A locally statistical active contour model (LACM) was proposed based on Aubert-Aujol (AA) denoising model and variational level set method. Then, the proposed model was transformed into a global optimization model by using convex relaxation technique. Finally, the split Bregman technique was applied to transform the global optimization model into two alternating optimization processes of Shrinkage operator and Laplace operator. The segmenting experiments of synthetic images and Envisat SAR images show that, the proposed globally segmentation model can not only obtain a stationary global minimum quickly, but also get the image segmentation boundary more accurately than classic models.