基于一般散射模型的Hybrid Freeman/Eigenvalue分解算法(英文)

A novel hybrid Freeman / eigenvalue decomposition with general scattering models

提出了一种新的基于一般散射模型的hybrid Freeman/eigenvalue分解算法,用于分析极化合成孔径雷达(PolS AR)数据。文中,单位矩阵作为体散射模型,相干矩阵的两个较大特征值对应的特征向量作为表面散射模型和二次散射模型,并且不需要反射对称条件。新算法有三个优点:第一,表面散射和二次散射不需要反射对称条件,更符合一般散射体的建模;第二,因为散射能量是相干矩阵特征值的线性组合,所以散射能量具有旋转不变性;第三,表面散射能量和二次散射能量避免了负值现象。在San Francisco地区的AIRSAR数据上进行了实验,证明了新算法的有效性。

A novel hybrid Freeman / eigenvalue decomposition with general scattering models was proposed for polarimetric synthetic aperture radar( PolS AR) data. A unit matrix represents the volume scattering model,and eigenvectors corresponding to the two larger eigenvalues of the coherency matrix are used as the surface scattering model and double-bounce scattering model for non-reflection symmetry condition. There are three advantages in the proposed hybrid decomposition. Firstly,the surface and double-bounce scattering models are free from the reflection symmetry constraint which is more general and realistic for common media. Secondly,since the scattering powers of the proposed method are solved as linear combinations of the eigenvalues derived from the coherency matrix,they are all roll-invariant parameters. Thirdly,negative powers of surface scattering and double-bounce scattering are avoided with non-rotation of the coherency matrix. Fully PolS AR data on San Francisco are used in the experiments to prove the efficacy of the proposed hybrid decomposition.