Integrating Gross Error Identification with Deep Learning for InSAR Topography-Dependent Delay Correction: A Case Study of the Baihetan Hydropower Station Area

Tropospheric delay poses a major limitation to high-precision InSAR observations, particularly in high mountain and canyon regions. To address this issue, this study proposes a combined model (EiMLP) that integrates gross error identification with a multilayer perceptron (MLP) for topography-dependent tropospheric delay correction. The gross error identification module detects unwrapped phase jumps based on phase gradients, followed by an MLP model that reconstructs the atmospheric phase using unwrapped phase and elevation information from a single interferogram. The proposed method is validated in the Baihetan Hydropower Station area and compared with traditional correction methods. Experimental results demonstrate that the proposed method achieves a Structural Similarity Index Measure (SSIM) of 0.970 and a Root Mean Square Error (RMSE) of 0.673 rad for the simulated atmospheric phase. After atmospheric correction, the average phase standard deviation of the interferograms is reduced by 83%, and the topography-related correlation is significantly suppressed. Furthermore, after correction by the proposed method, the signals that are masked by atmospheric errors in the original interferograms can be clearly revealed, indicating the potential for slope instability. These findings indicate that the EiMLP model, operating on a single interferogram, exhibits robust applicability and provides a reliable reference for future InSAR tropospheric delay correction.