DP-JMRNet: A Deep Unfolding Network for Differential Phase Preservation in Sparse Bitemporal SAR Reconstruction

Abstract: 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.