SAR图像水域边缘检测中，传统算法由于不能较好地克服斑点噪声影响，因此检测出的虚假边缘较多。利用多尺度几何Shearlet变换对曲线精确有效检测等特点，通过改进Shearlet变换并结合聚类及Snake模型等方法，提出了一种新的SAR图像水域检测方法。实验结果表明，该方法不仅提高了边缘检测的完整性和精确性，而且有效克服了斑点噪声的影响，对SAR图像水域边缘的检测是有效可行的。

For SAR image waters edge detection, the traditional algorithm can not suppress speckle noise，so there are many false edges in the results. Based on Shearlet transform, a kind of multi-scale geometric transformations, which represent curves accurately and effectively, we propose an improved Shearlet transform. Combining improved Shearlet transform with clustering and snake model, a new method of waters edge detection for SAR image is performed. Experiments show that new method can not only reduce influence of speckle noise, but improve the integrity and accuracy of edge detection, and it is efficient and effective for SAR image waters edge detection.