Modified multiple-component scattering power decomposition for PolSAR data based on eigenspace of coherency matrix

基于相干矩阵特征空间的改进PolSAR数据多元散射能量分解方法

A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.

文章提出了一种改进的极化合成孔径雷达(PolSAR)数据多元散射能量分解方法。改进后的分解方法包括两个不同的步骤。首先,利用相干矩阵的特征向量对散射模型进行修正;其次,利用目标的熵和各向异性来提高体散射能量。该算法在保持城市区域中较高的二次反射散射能量的前提下,有效地提高了植被区域的体散射能量。利用真实的数据验证了该方法的有效性,将原始相干矩阵分解得到的散射能量和方向角补偿后相干矩阵分解的散射能量与三种已有的分解算法进行了对比。实验结果表明,文章提出的分解方法能够更有效地表示不同地物极化SAR数据集的散射能量。