一种新的基于非反射对称非负特征值分解的Freeman分解
doi: 10.3724/SP.J.1146.2012.00897
A Novel Freeman Decomposition Based on Nonnegative Eigenvalue Decomposition with Non-reflection Symmetry
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摘要: 该文首次推导出了在非反射对称情况下非负特征值分解(NNED)的分析解法，即非反射对称NNED；并将其应用于Freeman分解，提出了一种基于非反射对称NNED的Freeman分解。在Freeman分解中，非反射对称NNED用于提取体散射功率，并用于调整体散射、二面角散射以及表面散射功率以确保余项协方差矩阵没有负特征值。相比于基于反射对称NNED的Freeman分解，所提的分解方法有效地利用了在反射对称条件下被假定为0的非对角线元素，能保证余项协方差矩阵没有负特征值，实测极化SAR数据实验表明，所提的分解方法能显著地加强城区的二面角散射功率并且减少城区的体散射功率。
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关键词:
- 极化合成孔径雷达 /
- 极化目标分解 /
- Freeman分解 /
- 非负特征值分解(NNED) /
- 非反射对称
Abstract: An analytic solution to the Non-Negative Eigenvalue Decomposition (NNED) in the non-reflection symmetry case is derived for the first time, which is named as NNED with non-reflection symmetry. It is applied to the Freeman decomposition, and then a Freeman decomposition based on NNED with non-reflection symmetry is proposed. During the Freeman decomposition, the NNED with non-reflection symmetry is used to extract volume scattering power, and adjust volume scattering power, double-bounce scattering power and surface scattering power to ensure the remainder covariance matrix has no negative eigenvalues. Compared with the Freeman decomposition based on NNED with reflection symmetry, the proposed decomposition method can availably use the non-diagonal elements which are regarded as zeros in the reflection symmetry case, and can ensure the remainder covariance matrix has no negative eigenvalues. The real-POLSAR-data experiment shows the proposed decomposition method can markedly enhance the double-bounce scattering power and weaken the volume scattering power. -
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