Phase unwrapping is the process of retrieving the true phase values from observed wrapped phases by adding the correct multiples of 2π. This process is crucial in synthetic aperture radar (SAR) interferometry, and numerous studies have aimed to enhance its performance. This study explored phase unwrapping using a modified U-Net regression model by optimizing both the network structure and training data. For network structure optimization, the study first compared model performance based on the size ratio of the lowest feature maps, determined by the number of pooling layers, to the size of convolutional kernels. A multi-kernel U-Net structure was developed to ensure robustness against variations in phase noise and gradient, compared to a standard single-kernel U-Net. Regarding the training data, data augmentation was implemented to address imbalances and better represent the local noise characteristics found in actual SAR interferograms. The training data was simulated to include local noise effects based on coherence measurements from real SAR data, as well as simple noise used for benchmarking the unwrapping performance with different training datasets. The results indicated that when the convolutional kernel size is smaller than the feature map size at the lowest layer, increasing the number of pooling layers leads to improvements in unwrapping performance. Conversely, performance decreased when the feature map size at the lowest layers was smaller than the convolutional kernel size. Specifically, the single-kernel U-Net with six pooling layers and the multi-kernel U-Net with five pooling layers exhibited the best unwrapping performance. Considering both simulated and real synthetic aperture radar interferogram data, the mean absolute errors for the single- and multi-kernel U-Net trained with simple noise were approximately 0.235 and 0.254, respectively. In contrast, for models trained with locally variable noise simulation data, the MAEs dropped to about 0.033 and 0.032, showing an improvement by approximately eightfold over models trained with simple noise. For real synthetic aperture radar interferograms, the mean absolute errors were 0.542 (single-kernel U-Net trained using simple noise), 0.592 (multi-kernel U-Net trained using simple noise), 0.542 (single-kernel U-Net trained using local noise), and 0.445 (proposed), respectively, underscoring the significant impact of training data on unwrapping performance. The study also evaluated the performance of the statistical-cost, network-flow algorithm for phase unwrapping (SNAPHU), obtaining mean absolute errors of about 0.043 for simulation data and 0.861 for real SAR data. Consequently, the multi-kernel model trained with locally different noise simulation data demonstrated roughly twice the performance compared to the traditional phase unwrapping method.

中文翻译：

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通过训练数据模拟和网络结构优化对改进的 U 网 SAR 干涉图进行相位展开


相位展开是通过添加正确的 2π 倍数从观察到的缠绕相位中检索真实相位值的过程。这一过程对于合成孔径雷达 (SAR) 干涉测量至关重要，许多研究旨在提高其性能。本研究通过优化网络结构和训练数据，探索使用改进的 U-Net 回归模型进行相位展开。对于网络结构优化，该研究首先根据最低特征图的尺寸比（由池化层的数量决定）与卷积核的尺寸进行比较模型性能。与标准单内核 U-Net 相比，开发了多内核 U-Net 结构，以确保针对相位噪声和梯度变化的鲁棒性。关于训练数据，实施了数据增强以解决不平衡问题并更好地表示实际 SAR 干涉图中发现的局部噪声特征。训练数据经过模拟，包括基于真实 SAR 数据的相干性测量的局部噪声效应，以及用于对不同训练数据集的展开性能进行基准测试的简单噪声。结果表明，当卷积核尺寸小于最低层的特征图尺寸时，增加池化层的数量可以提高展开性能。相反，当最低层的特征图尺寸小于卷积核尺寸时，性能会下降。具体来说，具有六个池化层的单核 U-Net 和具有五个池化层的多核 U-Net 表现出最佳的展开性能。 考虑到模拟和真实合成孔径雷达干涉图数据，使用简单噪声训练的单核和多核 U-Net 的平均绝对误差分别约为 0.235 和 0.254。相比之下，对于使用局部可变噪声模拟数据训练的模型，MAE 降至约 0.033 和 0.032，与使用简单噪声训练的模型相比，MAE 提高了约八倍。对于真实合成孔径雷达干涉图，平均绝对误差为 0.542（使用简单噪声训练的单核 U-Net）、0.592（使用简单噪声训练的多核 U-Net）、0.542（使用简单噪声训练的单核 U-Net）分别为 0.445（局部噪声）和 0.445（建议），强调了训练数据对展开性能的显着影响。该研究还评估了相位展开的统计成本网络流算法 (SNAPHU) 的性能，获得模拟数据的平均绝对误差约为 0.043，真实 SAR 数据的平均绝对误差约为 0.861。因此，使用局部不同的噪声模拟数据训练的多核模型表现出比传统相位展开方法大约两倍的性能。