PolSAR Coherency Matrix Decomposition Based on Constrained Sparse Representation
PolSAR Coherency Matrix Decomposition Based on Constrained Sparse Representation
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DOI：
10.1109/tgrs.2013.2293663
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发表时间：
2014-01
影响因子：
8.2
通讯作者：
Yinghua Wang;Hongwei Liu;B. Jiu
中科院分区：
文献类型：
--
作者：
Yinghua Wang;Hongwei Liu;B. Jiu
本文提出了一种新的基于模型的极化合成孔径雷达相干矩阵分解方法。我们从以下两个方面提高模型的灵活性：为了达到模型的灵活性和计算复杂性之间的折衷，对于体积散射分量，允许基本散射体的形状从球/平板到偶极子，然后二面角，而取向随机性通过只考虑两种情况来简化。对于每个组件考虑不同的定向角度。由于模型变得更加复杂，开发了新的分解程序。首先将三分量分解转化为一个约束稀疏表示问题。然后，受Bruckstein等人在2008年开发的正交匹配追踪变体的启发，设计了新的分解过程。使用合成数据集和两个真实的SAR数据集，包括RADARSAT-2数据集和美国宇航局/喷气推进实验室AIRSAR数据集在弗朗西斯科湾的有效性进行了验证。
This paper presents a new model-based decomposition method for the polarimetric synthetic aperture radar coherency matrices. We improve the model flexibility from the following two aspects: To reach a compromise between model flexibility and computation complexity, for the volume scattering component, the elementary scatterer shape is allowed to change from sphere/flat plate to dipole, then to dihedral, whereas orientation randomness is simplified by only considering two cases. Different orientation angles are considered for each component. Since the models become more complex, new decomposition procedures are developed. The three-component decomposition is first reformulated as a constrained sparse representation problem. Then, inspired by the orthogonal matching pursuit variant developed by Bruckstein et al. in 2008, new decomposition procedures are designed. The effectiveness of the proposed method is verified using a synthetic data set and two real SAR data sets, including a RADARSAT-2 data set and the NASA/JPL AIRSAR data set over San Francisco Bay.