用几何感知网络提升雷达相位恢复，跨区域无训练泛化能力强。
FiLM-GPNet: Geometry-Aware Pseudo-Supervised Phase Restoration with Zero-Shot Generalization for Large Temporal InSAR Stacks
- 基于特征调制和7维几何描述符，自适应不同成像条件。
- 相位残差降低68%~66%，去缠绕成功率提升7.7个百分点。
- 无需重新训练即可跨区域应用，适合复杂地形监测场景。
密集商业合成孔径雷达（SAR）时序数据推动了时间干涉SAR（InSAR）分析的发展，但固定经典滤波器在异构成像几何下表现不佳，影响相位质量和时序一致性。本文提出FiLM-GPNet，一种基于几何条件的包裹相位恢复网络，通过特征逐项线性调制（FiLM）和每对干涉图的7维几何描述符显式适应采集差异。模型利用黄金标准滤波后的干涉图进行伪监督训练，并通过三元组闭合一致性施加干涉物理正则化，同时估计像素级偶然不确定性。在三个来自IEEE GRSS 2026数据融合竞赛的Capella聚光堆栈上实验表明，与黄金标准基线相比，该方法在夏威夷和西澳大利亚分别将时序残差减少68%和66%，闭合误差分别降低10%和13%；在西澳大利亚还提升了7.7个百分点的去缠绕成功率和31%的数字高程模型（DEM）归一化中位绝对偏差（NMAD）。模型在未重新训练的情况下，对地理和几何差异显著的第三个堆栈（洛杉矶）也表现出强零样本泛化能力，验证了几何条件化恢复作为异构堆栈中固定经典滤波替代方案的有效性。
原文摘要 · Abstract (English)
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.
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