综合多特征的高分辨率极化SAR图像分割

Integrated multi-feature segmentation method for high resolution polarimetric SAR images

针对高空间分辨率全极化数据的特点,基于分形网络演化分割算法框架,本文提出了一种综合K分布统计特征、Pauli分解特征和空间形状特征的高分辨率全极化SAR图像分割方法。该方法采用对数似然函数定义K分布统计特征异质度,对Pauli分解特征加权定义极化分解特征异质度。在此基础上,综合统计、极化分解和形状特征构建对象相似性准则,建立高分辨率全极化SAR图像多特征综合分割流程。通过模拟数据和ESAR全极化数据实验并与其他分割方法比较,验证了本文分割方法的有效性。

This paper proposed a novel segmentation method which integrates statistical distribution, geometric shape features and polarimetric decomposition features for high resolution polarimetric synthetic aperture radar （SAR） data. This method is based on the fractal network evolution algorithm （FNEA） that integrates K distribution statistics and Pauli decomposition features. Specifically, statistical heterogeneity of objects is defined by the maximum log likelihood function based on K distribution. Polarimetric decomposition heterogeneity of objects is calculated through the weighted sum of standard deviation of Pauli decomposition features. A total heterogeneity of objects is defined by the weighted sum of statistical heterogeneity, polarimetric decomposition heterogeneity and shape heterogeneity. Then, the multi feature segmentation procedure for high resolution po larimetric SAR data is constructed. The effectiveness of the integrated multi feature segmentation we develope is demonstrated by simulated data and L band E-SAR polarimetrie data.