适用于高分辨SAR图像的全局稳态最小水平集分割方法

该文针对高分辨率SAR图像的分割问题提出了一种新的快速的水平集方法。该方法基于G0分布能够同时描述高分辨率和中低分辨率条件下的SAR图像统计特性，通过水平集方法求解能量泛函最小化实现SAR图像的分割。由于能量泛函被设计为具有全局稳态最小值，使得该方法具有较好的全局分割能力和比较快的分割速度，从而增强了该方法的实用性。利用模拟和真实SAR图像上的分割实验验证了该方法的有效性。

A Global Stationary Minimum Level Set Segmentation Method for High-resolution SAR Images

A new and fast level set method for segmentation of high-resolution SAR images into statistical homogenous areas is proposed. This approach is based on the G⁰ statistical model which can describe high-resolution SAR images as well as low-resolution SAR images. And the segmentation is obtained by minimizing energy function with level set methods. As the energy functional is designed to have global stationary minimum, a global and fast segmentation technique can be obtained, thus the practicality of the proposed approach is enhanced. The performance of the algorithm is verified with experiments based on both synthetic and real SAR images.