An Adaptive Error Correction Method for InSAR Data Processing Guided by Deformation Prior Information

Interferometric synthetic aperture radar (InSAR) is a crucial technology for monitoring large-scale surface deformation. Recent advancements have increasingly emphasized the automation of InSAR data processing. However, terrain complexity, environmental variability, and diverse deformation patterns in wide-area monitoring introduce multiple error sources. Conventional correction models based on singular assumptions struggle to achieve adaptive processing, often resulting in low processing efficiency and distortion of some deformation results. To address these challenges, this study proposes an adaptive error correction method guided by deformation prior information, enhancing automated workflows for wide-area InSAR processing. By integrating prior deformation and terrain information to create mask files, this method adaptively distinguishes deformation signals from error components, enabling precise error correction. A case study conducted in the North China Plain (NCP) demonstrates the method’s adaptive error-correction capabilities. Experimental results indicate that the proposed method achieves robust error separation while maintaining solution accuracy across deformation scales from regional subsidence to localized deformations. This method provides novel algorithmic support for automated InSAR data processing in wide-area applications, significantly improving processing efficiency and result reliability.