基于改进C-V水平集模型的SAR图像分割
SAR IMAGE SEGMENTATION BASED ON IMPROVED LEVEL SET C-V MODEL

本文研究了SAR图像分割的问题.利用一种加入图像边缘信息且无需重新初始化的改进水平集方法,获得了比传统C-V模型分割速度更快、准确度更高的分割结果.推广了C-V水平集模型分割灰度不均匀的SAR图像以及零水平集曲线的初始化等结果.
In this paper, we study the problem of SAR image segmentation. By using an improved level of joining the image edge information and without reinitialization set method, we obtain the results that the improved C-V level set model is faster and more accurate segmentation than the C-V level set model. Generalization of C-V level set model of how efiective segmentation of SAR image gray uniform as well as the zero level set curve such as the initialization results. It extends the results of segmentation of the SAR image of gray uneven using the C-V level set model, and the zero level set curve initialization