Mitigating decorrelation effects on interferometric synthetic aperture radar (InSAR) time series data is challenging. The phase linking (PL) algorithm has been the key to handling signal decorrelations in the past 15 years. Numerous studies have been carried out to enhance its precision and computational efficiency. Different PL algorithms have been proposed, each with unique phase optimization approaches, such as the quasi-Newton method, equal-weighted and coherence-weighted factors, component extraction and selection SAR (CAESAR), and eigendecomposition-based algorithm (EMI). The differences among the PL algorithms can be attributed to the weight criteria adopted in each algorithm, which can be coherence-based, sparsity-based, or other forms of regularization. The PL algorithm has multiple applications, including SAR tomography (TomoSAR), enhancing distributed scatterers (DSs) to combine with persistent scatterers (PS) in PS and DS (PSDS) techniques, and compressed PSDS InSAR (ComSAR), where it facilitates the retrieval of the optimal phase from all possible measurements. This article aims to review PL techniques developed in the past 15 years. The review also underscores the importance of the PL technique in various SAR applications (TomoSAR, PSDS, and ComSAR). Finally, the deep learning (DL) approach is discussed as a valuable tool to improve the accuracy and efficiency of the PL process.

中文翻译：

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干涉相位链接：算法、应用和视角

减轻干涉合成孔径雷达 (InSAR) 时间序列数据的去相关效应具有挑战性。过去 15 年里，相位链接 (PL) 算法一直是处理信号去相关的关键。为了提高其精度和计算效率，已经进行了大量研究。人们已经提出了不同的 PL 算法，每种算法都具有独特的相位优化方法，例如拟牛顿法、等权重和相干权重因子、分量提取和选择 SAR (CAESAR) 以及基于特征分解的算法 (EMI)。PL 算法之间的差异可以归因于每种算法采用的权重标准，可以是基于相干性、基于稀疏性或其他形式的正则化。PL 算法具有多种应用，包括 SAR 层析成像 (TomoSAR)、增强分布式散射体 (DS) 以与 PS 和 DS (PSDS) 技术中的持久散射体 (PS) 相结合，以及压缩 PSDS InSAR (ComSAR)，它有助于检索从所有可能的测量中得出最佳相位。本文旨在回顾过去 15 年开发的 PL 技术。该评论还强调了 PL 技术在各种 SAR 应用（TomoSAR、PSDS 和 ComSAR）中的重要性。最后，讨论了深度学习 (DL) 方法作为提高 PL 过程的准确性和效率的宝贵工具。