A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model
This work addresses segmentation challenges in SAR imaging, which is important for remote sensing applications, but it appears incremental as it builds on existing denoising and optimization techniques.
The authors tackled SAR image segmentation with intensity inhomogeneity by proposing a locally statistical active contour model and transforming it into a global optimization model, resulting in fast algorithms that outperform state-of-the-art models in experiments on synthetic and Envisat SAR images.
In this paper, we propose a novel locally statistical active contour model (LACM) based on Aubert-Aujol (AA) denoising model and variational level set method, which can be used for SAR images segmentation with intensity inhomogeneity. Then we transform the proposed model into a global optimization model by using convex relaxation technique. Firstly, we apply the Split Bregman technique to transform the global optimization model into two alternating optimization processes of Shrink operator and Laplace operator, which is called SB_LACM model. Moreover, we propose two fast models to solve the global optimization model , which are more efficient than the SB_LACM model. The first model is: we add the proximal function to transform the global optimization model to a general ROF model[29], which can be solved by a fast denoising algorithm proposed by R.-Q.Jia, and H.Zhao; [29] is submitted on 29-Aug-2013, and our early edition ever submitted to TGRS on 12-Jun-2012, Venkatakrishnan et al. [30] proposed their 'pnp algorithm' on 29-May-2013, so Venkatakrishnan and we proposed the 'pnp algorithm' almost simultaneously. Thus we obtain a fast segmentation algorithm with global optimization solver that does not involve partial differential equations or difference equation, and only need simple difference computation. The second model is: we use a different splitting approach than one model to transform the global optimization model into a differentiable term and a general ROF model term, which can be solved by the same technique as the first model. Experiments using some challenging synthetic images and Envisat SAR images demonstrate the superiority of our proposed models with respect to the state-of-the-art models.