A Novel Knowledge-Learning Coupling Method for InSAR Phase Unwrapping of Large Surface Displacements in Coal Mining Areas

Underground mining activities often lead to large local surface displacements. In this case, the interferometric fringes are dense, the deformation gradient between adjacent pixels tends to exceed $\pi $ , and traditional phase unwrapping (PU) methods that satisfy the phase continuity assumption have difficulty correctly retrieving the deformation. Deep learning-based PU methods can overcome the phase continuity assumption to a certain extent. However, deep learning-based PU methods also have shortcomings, such as weak generalization, difficulty in transfer, and lack of interpretability. To address these issues, this article presents a new PU method for large gradient deformation of mining areas that couples knowledge and a deep learning network (KLC-Net). This method integrates the knowledge of the mining area subsidence mechanism and the interferometric synthetic aperture radar (InSAR) phase prior knowledge into the learning network and constructs a knowledge-learning coupling framework of input sample constraints, objective function constraints, and network structure constraints. The simulation experiments show that when the noise level (NL) is less than $\pi $ , the KLC-Net algorithm is suitable for interferograms with different imaging geometries. The actual engineering experimental results show that even under severe temporal decorrelation conditions, the KLC-Net algorithm can still effectively retrieve surface deformations up to 1.6 m with an average root mean square error (RMSE) of 23.5 mm. These experimental results show that the KLC-Net algorithm can effectively improves the ability and accuracy of PU under large gradient deformations in coal mining areas, and also improves the generalization performance and interpretability of deep learning-based PU methods.