发表机构
Capella Space; Jet Propulsion Laboratory, California Institute of Technology（卡佩拉空间; 加州理工学院喷气推进实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Whirlwind，一种基于概率成本的最小费用流二维相位解缠算法，通过固定似然比成本和高效求解器，在保持解质量的同时显著提升速度和降低内存使用，适用于大规模InSAR数据处理。
AI 中文摘要
在广域干涉合成孔径雷达（InSAR）处理中，二维相位解缠可能主导计算和内存使用。本文提出Whirlwind，一种针对大尺寸地表形变干涉图量身定制的最小费用流（MCF）解缠器。我们利用多视干涉相位统计推导出固定的似然比成本，并对局部相位梯度和相干性估计中的不确定性进行边缘化。一个紧凑的整数查找表和带有Dial桶队列的并行增广连续最短路径求解器使得MCF求解高效，同时避免了依赖于流量的成本重新线性化。我们测试了这些速度和内存的改进是否能在不损失解质量的情况下获得，使用了针对单个干涉图、任务生产规模以及端到端位移时间序列解决方案的多种评估标准。在2019年Ridgecrest地震的74百万像素Sentinel-1干涉图上，Whirlwind运行时间为209秒，比单块SNAPHU快28倍，同时使用其峰值内存的40%。在6,014个临时NISAR地理编码解缠（GUNW）产品中，93%的产品在超过99%的有效像素上与SNAPHU得出的结果一致；在七个具有挑战性的77兆赫南极帧上，Whirlwind的中位运行速度比SNAPHU快21倍，同时匹配其99.4%的整数周期分配。最后，在一个完整的Sentinel-1时间序列实验中，Whirlwind比SNAPHU提高了网络反演一致性，在葡萄牙本德滑坡的高分辨率Capella时间序列中，它在五种解缠配置中与密集GPS网络的相关性最高。Whirlwind作为开源库提供，能够处理全球日益增长的InSAR数据量。
英文摘要
Two-dimensional phase unwrapping can dominate computation and memory usage in wide-area Interferometric Synthetic Aperture Radar (InSAR) processing. Here we present Whirlwind, a minimum-cost-flow (MCF) unwrapper tailored for large interferograms of surface deformation. We derive fixed likelihood-ratio costs using multilook interferometric phase statistics, marginalizing uncertainty in the local phase-gradient and coherence estimates. A compact integer lookup table and parallel-augmenting successive-shortest-path solver with Dial bucket queues make the MCF solve efficient while avoiding flow-dependent cost relinearization. We test whether these improvements in speed and memory can be obtained without a loss of solution quality using multiple evaluation criteria for individual interferograms, at mission-production scale, and in end-to-end displacement time series solutions. On a 74-Mpixel Sentinel-1 interferogram of the 2019 Ridgecrest earthquakes, Whirlwind runs in 209 s, 28x faster than single-tile SNAPHU, while using 40% of the peak memory. Across 6,014 provisional NISAR geocoded unwrapped (GUNW) products, 93% agreed with the SNAPHU-derived results on more than 99% of valid pixels; on seven challenging 77-MHz Antarctic frames, Whirlwind ran a median 21x faster than SNAPHU while matching 99.4% of its integer-cycle assignments. Finally, in a complete Sentinel-1 time series experiment, Whirlwind improved the network-inversion consistency over SNAPHU, and in a high-resolution Capella time series of the Portuguese Bend landslide, it achieved the highest correlation with a dense GPS network among five unwrapping configurations. Whirlwind is provided as an open-source library capable of handling the growing volume of globally available InSAR data.
