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An Improved Hybrid Freeman/Eigenvalue Decomposition For Polarimetric SAR Data

机译:极化SAR数据的改进的混合Freeman /特征值分解

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In this paper, the negative power problem in hybrid Freeman/Eigenvalue decomposition is analyzed. To solve this problem, an improved hybrid Freeman/Eigenvalue polarization decomposition method is proposed. Firstly, orientation angle is compensated and phase angle is rotated of coherent matrix through judging dominant scattering mechanism. Secondly, considering the continuous variation of HH and VV ratio, a generalized volume scattering model is introduced into the hybrid decomposition method. Finally, negative power problem in the hybrid Freeman/Eigenvalue decomposition method is further solved by using the nonnegative eigenvalue method. The results are verified by using ESAR data from Oberpfaffenhofen area in Germany. The results show that this method can effectively solve the problem of negative power and the decomposition result is easy to be combined with the actual feature.
机译:本文分析了混合Freeman /特征值分解中的负幂问题。为了解决这个问题,提出了一种改进的混合Freeman /特征值极化分解方法。首先,通过判断主导散射机制,对相干矩阵的取向角进行补偿,使相角旋转。其次,考虑到HH和VV比的连续变化,将广义体积散射模型引入到混合分解方法中。最后,通过非负特征值方法进一步解决了混合Freeman /特征值分解方法中的负功率问题。使用德国Oberpfaffenhofen地区的ESAR数据验证了结果。结果表明,该方法可以有效解决负电源问题,分解结果易于与实际特征相结合。

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