A Non-Local InSAR Phase Filtering Method Based on Deep Learning

Phase filtering based on deep learning is currently one of the hot research directions in the processing of interferometric SAR data. In this paper, a filtering model is designed using a deep learning framework and the non-local characteristics of interferometric phase maps. The method divides the interferometric phase map into image patches, with each undergoing feature extraction via structurally identical, weight-sharing encoders and decoders to leverage the map’s non-local similarities. Experimental outcomes show this approach outperforms conventional spatial-domain, transform-domain, and deep learning-based filtering methods.