Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar(SAR) images. A novel method is proposed based on integrating the geometric active contour(GAC) and the support vector machine(SVM)models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items: the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models. A novel method is proposed based integrating the geometric active contour (GAC) and the support vector machine (SVM) models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items: the likelihood and the distance between the initial segmentation and the evolving contour Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.