论文标题
无监督的生成神经方法，用于INSAR相滤波和相干估计
An Unsupervised Generative Neural Approach for InSAR Phase Filtering and Coherence Estimation
论文作者
论文摘要
相滤波和像素质量（相干）估计对于从干涉合成孔径雷达（INSAR）图像产生数字高程模型（DEM）至关重要，因为它消除了空间上的不一致（残基）并极大地改善了随后的未包装。大量的INAR数据促进了地理区域的广泛监测（WAM）。并行计算的进步加速了卷积神经网络（CNN），使它们比人类在视觉模式识别方面具有优势，这使CNNS成为WAM的不错选择。然而，这项研究在很大程度上尚未探索。因此，我们提出了一种基于CNN的生成模型“ Geninsar”，用于关节相滤波和相干估计，该模型直接学习INSAR数据分布。 Geninsar对卫星和模拟嘈杂的Insar图像的无监督训练优于其他五种相关方法，总残留物减少（平均要高16.5％），分支切割周围的过度平滑/人工制品较少。与相关方法相比，GENINSAR的相位和相干均方根误差和相位余弦误差的平均改善分别为0.54、0.07和0.05。
Phase filtering and pixel quality (coherence) estimation is critical in producing Digital Elevation Models (DEMs) from Interferometric Synthetic Aperture Radar (InSAR) images, as it removes spatial inconsistencies (residues) and immensely improves the subsequent unwrapping. Large amount of InSAR data facilitates Wide Area Monitoring (WAM) over geographical regions. Advances in parallel computing have accelerated Convolutional Neural Networks (CNNs), giving them advantages over human performance on visual pattern recognition, which makes CNNs a good choice for WAM. Nevertheless, this research is largely unexplored. We thus propose "GenInSAR", a CNN-based generative model for joint phase filtering and coherence estimation, that directly learns the InSAR data distribution. GenInSAR's unsupervised training on satellite and simulated noisy InSAR images outperforms other five related methods in total residue reduction (over 16.5% better on average) with less over-smoothing/artefacts around branch cuts. GenInSAR's Phase, and Coherence Root-Mean-Squared-Error and Phase Cosine Error have average improvements of 0.54, 0.07, and 0.05 respectively compared to the related methods.
