AI 中文总结
针对稀疏双时相SAR重建的差分相位保留问题，提出DP-JMRNet深度展开网络，在多采样率下实现更低差分相位RMSE，且参数更少，在模拟数据和Sentinel-1数据上均验证了有效性。
AI 中文摘要
合成孔径雷达（SAR）复图像通常主要通过幅度进行可视化和评估，相位虽保留在复数据中，但很少被视为直接的图像质量目标。现有的稀疏重建方法通常聚焦于幅度保真度和单时相复图像重建精度，然而，两次 acquisitions（采集）之间的相位差是InSAR（合成孔径雷达干涉测量）中视线形变反演的驱动因素，涵盖地面沉降监测到地震形变测绘等应用。本文提出面向差分相位的联合掩膜重建网络（DP-JMRNet），该网络采用深度展开方法，基于差分相位目标从掩膜观测中联合重建两个时相的数据。其中，交换等变交互模块使重建结果与时相顺序无关；相干感知门在相干区域开启跨时相共享，在两个时相不一致的区域关闭共享。在模拟双时相SAR数据上，DP-JMRNet在30%、40%和50%采样率下均取得最低的差分相位均方根误差（RMSE），同时保持具有竞争力的幅度和复图像保真度，与最优基线方法相比，差分相位RMSE降低了47.5%至51.3%，且仅使用基线方法三分之一的参数。在三景Sentinel-1数据上验证了相同的趋势。对采集设计的系统研究进一步表明，两个时相共享相同的孔径支撑是相位保真度的必要条件，而优化采样掩膜并不能提升差分相位性能。代码和数据可在该https URL获取。
英文摘要
Complex SAR imagery is usually visualized and evaluated mainly through its magnitude. Phase is retained in the complex data but is rarely treated as a direct image-quality objective. Existing sparse reconstruction methods typically focus on magnitude fidelity and single-epoch complex reconstruction accuracy. However, the phase difference between two acquisitions is what drives line-of-sight deformation retrieval in InSAR, from ground subsidence monitoring to earthquake deformation mapping. This paper proposes the Differential-Phase-Oriented Joint Masked Reconstruction Network (DP-JMRNet), which uses deep unfolding to reconstruct the two epochs jointly from masked observations under a differential-phase objective. An exchange-equivariant interaction module makes the reconstruction independent of epoch ordering. A coherence-aware gate opens cross-epoch sharing in coherent regions and closes it where the two epochs disagree. On simulated bitemporal SAR data, DP-JMRNet attains the lowest differential-phase RMSE at 30\%, 40\%, and 50\% sampling rate, while maintaining competitive amplitude and complex-image fidelity. This corresponds to a 47.5\%--51.3\% reduction over the best baseline, achieved with one third of its parameters. The same trend is validated on three Sentinel-1 scenes. A systematic study of acquisition design further shows that sharing the same aperture support across epochs is necessary for phase fidelity, whereas optimizing the sampling mask does not improve the differential phase. The code and data are available at https://github.com/JasonBao05/coherent-sar-unfolding.
