The geometric active contour model is a classical image segmentation model based on the curve evolution theory and the level set method

which has been successfully applied to the segmentation of medical images. Due to the existence of speckle noise

the model fails in SAR image segmentation. Moreover

there are several disadvantages with this model. First

the evolution equation isn’t obtained with the energy minimization method. Second

the level set function needs to be reinitialized to a signed distance function periodically during the evolution. Finally

the model is computationally inefficient. Based on SAR image edge detectors and the variational level set method

a novel geometric active contour model is proposed under the criterion of energy minimization. The basic idea is that the energy functional is defined directly on the level set function and the original edge indicator function based on gradients is replaced with a new edge indicator function based on the ROEWA operator. Thus

the ability of detecting edges and the accuracy of locating edges are greatly increased

which makes the model very appropriate for SAR image segmentation. In addition

a term penalizing the level set function is added to the energy functional in order to force the level set function to be close to a signed distance function and therefore completely eliminates the need of the costly re-initialization procedure. Thanks to the contribution of this term

the numerical calculation of the model can be implemented by a simple explicit difference scheme; at the same time the evolution speed keeps very fast. The proposed model has several advantages. For example

it can be easily implemented; it results in accurate segmentation boundaries; it converges fast and its level set function doesn’t need to be reinitialized. The experimental results on the simulated image and real data show its efficiency and accuracy.