一种极化SAR协方差矩阵综合四分量分解模型

An Integrated Four-Component Model-Based Decomposition of Polarimetric SAR with Covariance Matrix

基于多视协方差矩阵发展了一种综合选择性去取向和广义体散射的极化SAR四分量分解模型。首先引入交叉极化相关系数进行螺旋体散射抑制和非反射对称地物去取向;然后采用一种随HH和VV功率比值自适应变化的广义体散射模型来替代原体散射模型;最后通过功率限制处理以完全消除分解负功率像素,该处理不仅能够保持地物主导散射类型不变,而且包含与Krogager分解三分量对应的非相干分解。通过机载L波段ESAR和AirSAR极化数据实验并与其他分解模型的比较,验证了该分解模型的有效性。

In this paper,we developed a four-component model-based incoherent decomposition with covariance matrix integrating selective de-orientation and generalized volume scattering.Firstly,a new cross-polarization coefficient is proposed which can be used for suppressing the helix scattering and de-orientating the non-reflection symmetric targets.Secondly,Yamaguchi decomposition is improved through adopting ageneralized volume scattering model instead of the original one.The new volume model can vary adaptively with the power ratio between HH and VV.Lastly,the power constrain is utilized to eliminate the negative power completely.The effectiveness of four-component decomposition is demonstrated Lband E-SAR and AirSAR polarimetric data.The results suggest that cannot only eliminate the negative phenomenon completely,but also can enhance double-bounce scattering in urban areas remarkably.It can effectively suppress the helix components which are difficult to produce in most natural areas.