A level set method for segmentation of high-resolution polarimetric SAR images using a heterogeneous clutter model
A level set method for segmentation of high-resolution polarimetric SAR images using a heterogeneous clutter model
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DOI：
10.1080/2150704x.2015.1058984
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发表时间：
2015-06
影响因子：
2.3
通讯作者：
Pengfei Zou;Zhen Li;B. Tian;Lijie Guo
中科院分区：
文献类型：
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作者：
Pengfei Zou;Zhen Li;B. Tian;Lijie Guo
针对高分辨率极化合成孔径雷达（PolSAR）图像中存在的强相干斑和纹理问题，提出了一种基于异质杂波模型的水平集分割方法。由于KummerU分布具有描述均匀和非均匀场景下PolSAR图像统计特性的能力，因此本文采用KummerU分布代替传统的Wishart分布作为PolSAR图像能量函数的统计模型，以提高分割精度。此外，为了降低计算强度，增强的距离正则化水平集演化（DRLSE-E）项被应用，以提高计算效率。利用合成和真实的PolSAR图像进行的实验结果表明，该方法比基于Wishart分布的水平集方法具有10%的精度。它也表明，添加DRLSE-E项减少了约三分之一的计算时间，从而证明了我们的方法的有效性。
To overcome the problem of strong speckle and texture in high-resolution polarimetric synthetic aperture radar (PolSAR) images, a novel level set segmentation method that uses a heterogeneous clutter model is proposed in this article. Because the KummerU distribution has the capability to describe the statistics of PolSAR imagery in both homogeneous and heterogeneous scenes, it is used to replace the traditional Wishart distribution as the statistical model that defines the energy function for PolSAR images in order to improve the accuracy of the segmentation. Moreover, in order to reduce the computation intensity, an enhanced distance-regularized level set evolution (DRLSE-E) term is applied to improve the computational efficiency. The experimental results obtained using synthetic and real PolSAR images show that the method described has an accuracy 10% better than level set methods based on Wishart distributions. It is also shown that adding the DRLSE-E term reduces the computation time by about a third, thus demonstrating the effectiveness of our method.