用深度展开网络联合重建稀疏雷达影像，精准保留形变检测关键的相位差。
DP-JMRNet: A Deep Unfolding Network for Differential Phase Preservation in Sparse Bitemporal SAR Reconstruction
- 通过深度展开框架，联合优化两个时相的复数图像重建。
- 在30%-50%采样率下，相位差均方误差降低47.5%-51.3%。
- 适用于高精度地表形变监测，尤其适合哨兵-1数据处理。
复杂SAR影像通常仅通过幅度进行可视化与评估，相位虽保留在复数数据中，却很少作为直接的图像质量目标。现有稀疏重建方法多关注幅度保真度和单时相复数重建精度，但干涉测量中线性视线形变的反演依赖于两时相间的相位差。本文提出面向相位差的联合掩码重建网络DP-JMRNet，利用深度展开技术，在差分相位目标下从掩码观测中联合重建双时相数据。设计了交换等变交互模块，使重建结果与时相顺序无关；引入相干感知门控机制，在相干区域开放跨时相信息共享，非一致区域则关闭。在模拟双时相SAR数据上，DP-JMRNet在30%、40%、50%采样率下均取得最低差分相位均方误差，同时保持优异的幅度与复数图像保真度。相较最优基线方法，误差降低47.5%–51.3%，且仅需其三分之一参数量。三个哨兵-1实测场景验证了相同趋势。系统性研究进一步表明，两时相共享天线孔径支撑是相位保真的必要条件，而优化采样掩码无法提升差分相位性能。代码与数据已开源。
原文摘要 · Abstract (English)
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.
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