The geometric active contourmodel is a classical image segmentationmodelbased on the curve evolution theory and the level setmethod, which has been successfully applied to the segmentation ofmedical images. Due to the existence of speckle noise,themodel fails in SAR image segmentation. Moreover, there are severaldisadvantageswith thismode.l Firs,t the evolution equation\nisn’tobtained with the energy minimizationmethod. Second, the level set function needs to be reinitialized to a signed distance function periodically during the evolution. Finally, themodel is computationally inefficien.t Based on SAR image edge detectors and the variational level setmethod, a novelgeometric active contourmodel is proposed under the criterion ofenergyminimization. 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 replacedwith a new edge indicator function based on the ROEWA operator. Thus, the ability ofdetecting edges and the accuracy of locating edges are greatly increased, whichmakes themodelvery appropriate forSAR image segmentation. In addition,a term penalizing the level setfunction is added to the energy functional in order to force the level setfunction 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 themodel can be implemented by a simple explicitdifference scheme; at the same time the evolution speed keeps very fas.t The proposedmodelhas severaladvantages.Forexample, itcan be easily implemented; itresults 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 realdata show its efficiency and accuracy.