有监督的水平集高分辨SAR图像分割方法

Supervised High-Resolution SAR Image Segmentation Algorithm Based on Level Set

针对基于统计模型的水平集SAR图像分割中参数估计耗时较多的问题,提出了一种有监督的高分辨SAR图像分割方法。该方法将Fisher分布和Gamma分布分别作为高分辨SAR图像的目标和背景统计模型,结合水平集方法推导了SAR图像分割水平集函数的能量泛函模型,通过最小化能量泛函得到曲线演化偏微分方程,实现对高分辨SAR图像的分割。试验结果表明,该方法对高分辨SAR图像具有强散射点的目标分割更完整,并且比无监督统计模型分割方法分割速度更快。

A new supervised level set segmentation method based on statistics model for high-resolution synthetic aperture radar（SAR） images is proposed.The target and background scattering statistics characteristic of the high-resolution SAR images is modeled by Fisher and Gamma probability density function separately,and an energy functional with respect to level set adapted for SAR image is defined.Partial differential equations（PDE） of curve evolution are obtained by minimizing the energy functional.Meanwhile,the parameters of the Fisher and Gamma distribution are estimated by training data selected in advance.The segmentation of the SAR images is implemented by the solution of the PDE.The performance of the method is verified by real SAR images.Results show that the method can get faster segmentation speed and more rounded target segmentation for targets with strong reflectors of high-resolution SAR images if only the training data are selected suitably.