A Nonlocal Filter for SAR Interferometric Phase Based on Partial Siamese Network

Filtering is a crucial step in synthetic aperture radar (SAR) interferometric phase processing. In the context of rapid development in deep learning, the interferometric phase filtering networks have demonstrated more powerful filtering capabilities than spatial and transform domain filtering models. Inspired by the deep learning-based and nonlocal filtering models, this article proposes a nonlocal SAR interferometric phase filtering model based on a partial siamese network. Within this model, the interferogram is decomposed into multiple patches, and each patch undergoes filtering using a set of structurally identical encoder–decoder networks. In order to make better use of the nonlocal characteristics of the interferogram to improve the filtering effect, the encoder–decoder networks corresponding to different patches share a portion of their weights. Finally, the filtered patches outputted from each decoder are concatenated through an aggregation block to obtain the complete filtered interferogram. The advantage of this framework lies in its ability to fully leverage the powerful feature learning capabilities of neural networks while maximizing the utilization of nonlocal characteristics in the interferogram. The filtering performance of the proposed method is tested through simulated interferograms and real-world interferograms obtained from the Sentinel-1 and Gaofen-3 mission. The experimental results demonstrate that compared to traditional spatial and transform domain filtering methods, as well as state-of-the-art deep learning-based and nonlocal filtering methods, the filtering approach proposed in this article achieves superior filtering performance.