中文摘要：

短程活动轮廓模型是一种基于变分法和偏微分方程的经典图像分割模型。由于乘性斑点噪声的存在，导致该模型不适用于SAR图像分割。基于SAR图像边缘检测算子，提出了一种新颖的短程活动轮廓模型。其基本思想是，将原有模型中基于梯度的边缘定位函数，替换为基于ROEWA算子的边缘定位函数，提高了模型的边缘检测能力和边缘定位精度，因而更适合于SAR图像的分割。另外，在新模型的能量泛函中增加“气球力”项，增强了曲线演化的动力，进而加快曲线演化的速度并降低模型对初始轮廓的敏感性。在模型的数值实现时，采用无条件稳定的AOS差分格式并辅以快速的水平集函数重新初始化算法，不但增强了模型的稳定性，而且还加快了模型的收敛速度。针对仿真图像、Radarsat和ERS实测数据的实验结果，验证了该模型的有效性和精确性。

英文摘要：

The geodesic active contour model is a classical image segmentation model based on the variational method and partial differential equations. Due to the existence of speckle noise, the model failed in SAR image segmentation. Based on SAR image edge detectors, a novel geodesic active contour model is proposed. The basic idea is that we use an edge indicator function based on the ROEWA operator replace the original edge indicator function based on gradients. Thus, the ability of detecting edges and the accuracy of locating edges are increased, which make the model more appropriate for SAR image segmentation. In addition, a ＂balloon force＂ term is added to the original model＇ s energy functional in order to enhance the power for curve evolution. As a result, the speed of curve evolution is increased and the sensitivity to the initial contour is reduced. In the numerical implementation of the model, an unconditionally stable AOS difference scheme and a fast algorithm for re-initialization of the level set function are adopted, which not only enhance the model＇ s stability, but also speed up the model＇s convergence. The experimental results on the simulated image, real Radasat and ERS data show its efficiency and accuracy.