发表机构
University of Oulu; University of Turku（奥卢大学; 图尔库大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出FiLM-GPNet网络，用于异质采集几何下的InSAR相位恢复，经实验验证其在时序残差、闭合误差等指标上优于Goldstein基线，且具备零样本泛化能力。
AI 中文摘要
密集商用合成孔径雷达（SAR）时间序列的日益可得性推动了时序干涉SAR（InSAR）分析，但固定经典滤波器在异质采集几何条件下失效，会降低相位质量与时序一致性。本文提出FiLM-GPNet，一种用于包裹相位恢复的几何条件网络，其通过特征-wise线性调制（FiLM）及每对样本的7维几何描述符，显式适配采集差异。该模型采用Goldstein滤波干涉图提供的伪监督进行训练，并通过三元组闭合一致性的干涉物理过程进行正则化，同时估计每个像素的偶然不确定性。在IEEE GRSS 2026数据融合竞赛的三个Capella聚束式堆栈上开展的实验表明，相较于Goldstein基线，FiLM-GPNet将夏威夷地区的时序残差降低68%、西澳大利亚地区降低66%，同时闭合误差分别降低10%和13%；在西澳大利亚，其还将解缠成功率提升7.7个百分点、数字高程模型（DEM）归一化中位数绝对偏差（NMAD）降低31%。该模型无需重新训练，即可对地理与几何特征显著不同的第三个堆栈（洛杉矶）展现出强零样本泛化能力，支持几何条件恢复作为异质堆栈中固定经典滤波的有效替代方案。
英文摘要
The growing availability of dense commercial Synthetic Aperture Radar (SAR) time series enables temporal Interferometric SAR (InSAR) analysis, but fixed classical filters fail under heterogeneous acquisition geometries, degrading phase quality and temporal consistency. We propose FiLM-GPNet, a geometry-conditioned network for wrapped-phase restoration that explicitly adapts to acquisition differences using Feature-wise Linear Modulation (FiLM) and a 7D per-pair geometry descriptor. The model is trained with pseudo-supervision from Goldstein-filtered interferograms and regularized by interferometric physics via triplet-closure consistency, while also estimating per-pixel aleatoric uncertainty. Experiments on three Capella Spotlight stacks from the IEEE GRSS 2026 Data Fusion Contest show that FiLM-GPNet reduces temporal residual by 68% (Hawaii) and 66% (Western Australia) relative to the Goldstein baseline, alongside closure error reductions of 10% and 13%, respectively. In Western Australia, it further improves unwrapping success rate by 7.7 percentage points and Digital Elevation Model (DEM) Normalized Median Absolute Deviation (NMAD) by 31%. The model also shows strong zero-shot generalization to a geographically and geometrically distinct third stack (Los Angeles) without retraining, supporting geometry-conditioned restoration as an effective alternative to fixed classical filtering across heterogeneous stacks.
CommentsIts IEEE Geoscience And Remote Sensing Symposium (IGARSS) 2026 Data Fusion Contest Winner Paper. reference https://www.grss-ieee.org/community/technical-committees/winners-of-the-2026-ieee-grss-data-fusion-contest-sar-temporal-storytelling/
