用扩散模型提升雷达形变反演的相位解缠精度，抗噪更强。
UnwrapDiff: A Conditional Diffusion Model for InSAR Phase Unwrapping
- 结合传统算法输出作为条件引导，用扩散模型修正相位解缠结果。
- 相比SNAPHU算法，平均降低10.11%的归一化均方误差。
- 在断层侵入等复杂场景下表现更优，适合地质形变监测应用。
相位解缠是合成孔径雷达干涉测量（InSAR）数据处理中的核心问题，支撑形变监测与灾害评估等地球物理应用。其可靠性受雷达观测中噪声和去相关影响，导致形变信号准确重建困难。本文提出基于去噪扩散概率模型（DDPM）的相位解缠框架UnwrapDiff，将传统最小代价流算法（SNAPHU）的输出作为条件引导。为评估鲁棒性，构建包含大气效应与多种噪声模式的合成数据集，模拟真实InSAR观测。实验表明，该模型利用条件先验的同时有效降低多种噪声影响，平均比SNAPHU降低10.11%的归一化均方误差（NRMSE），在如岩浆侵入等复杂情况下也实现更优重建质量。
原文摘要 · Abstract (English)
Phase unwrapping is a fundamental problem in InSAR data processing, supporting geophysical applications such as deformation monitoring and hazard assessment. Its reliability is limited by noise and decorrelation in radar acquisitions, which makes accurate reconstruction of the deformation signal challenging. We propose a denoising diffusion probabilistic model (DDPM)-based framework for InSAR phase unwrapping, UnwrapDiff, in which the output of the traditional minimum cost flow algorithm (SNAPHU) is incorporated as conditional guidance. To evaluate robustness, we construct a synthetic dataset that incorporates atmospheric effects and diverse noise patterns, representative of realistic InSAR observations. Experiments show that the proposed model leverages the conditional prior while reducing the effect of diverse noise patterns, achieving on average a 10.11\% reduction in NRMSE compared to SNAPHU. It also achieves better reconstruction quality in difficult cases such as dyke intrusions.
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