A Hybrid Polarimetric Target Decomposition Algorithm with Adaptive Volume Scattering Model
A Hybrid Polarimetric Target Decomposition Algorithm with Adaptive Volume Scattering Model
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DOI：
10.3390/rs14102441
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发表时间：
2022-05
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通讯作者：
Xiujuan Li;Yongxin Liu;Pingping Huang;Xiaolong Liu;W. Tan;W. Fu;Chunming Li
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文献类型：
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作者：
Xiujuan Li;Yongxin Liu;Pingping Huang;Xiaolong Liu;W. Tan;W. Fu;Chunming Li
已有的研究表明，基于模型的极化目标分解算法即使进行了去定向处理，仍然存在散射机制模糊和负功率问题。一个可能的原因是模型本身的动态范围有限，不能完全满足混合场景。为了解决这些问题，我们提出了一种混合极化目标分解算法（GRH）与广义体散射模型（GVSM）和随机粒子云体散射模型（RPCM）。GRH中使用的自适应体散射模型结合GVSM和RPCM来分别模拟由双反弹散射和表面散射主导的区域的体散射分量，以扩展模型的动态范围。此外，GRH根据全极化合成孔径雷达（PolSAR）数据的目标主导散射机制，自适应地选择GVSM和RPCM之间的体散射分量。使用旧金山弗朗西斯科的AirSAR数据集验证了该方法的有效性。比较研究进行了测试GRH的性能超过几个目标分解算法。实验结果表明，GRH算法在分解精度和负幂像素数量上均优于本文所测试的算法，表明GRH算法能够有效避免机构歧义和负幂问题。
Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in GRH incorporates GVSM and RPCM to model the volume scattering component of the regions dominated by double-bounce scattering and the surface scattering, respectively, to expand the dynamic range of the model. In addition, GRH selects the volume scattering component between GVSM and RPCM adaptively according to the target dominant scattering mechanism of fully polarimetric synthetic aperture radar (PolSAR) data. The effectiveness of the proposed method was demonstrated using AirSAR dataset from San Francisco. Comparison studies were carried out to test the performance of GRH over several target decomposition algorithms. Experimental results show that the GRH outperforms the algorithms we tested in this study in decomposition accuracy and reduces the number of negative power pixels, demonstrating that the GRH can significantly avoid mechanism ambiguity and negative power issues.