Objectives With the continuous accumulation of synthetic aperture radar (SAR) images, interferometric synthetic aperture radar (InSAR) technology is facing a computational bottleneck caused by the surge in the number of SAR images when processing wide area and long-term surface deformation monitoring tasks. Especially for the distributed scatterers InSAR (DS InSAR) method, the full combination strategy of interferometric pairs results in an extremely time-consuming solution process, limiting its widespread application in large-scale deformation monitoring. However, temporal InSAR data often contains a large amount of redundant information in the spatiotemporal dimension. Removing redundant information through image compression has become an effective solution.

Methods This paper proposes a tensor decomposition⁃based method for wide area long time series InSAR image compression. The proposed method utilizes the consistency of spatial pixel statistical properties to reconstruct the covariance matrix into a third-order tensor, and uses Tucker decomposition to achieve spatiotemporal data compression.

Results To verify its effectiveness, Sentinel-1A images from the main urban area of Kunming city are selected for the experiment. The results show that under the compression subspace settings of 2×3 and 2×5, the processing efficiency is improved by about 24 times and 40 times, respectively, and the deformation inversion accuracy still meets the monitoring requirement. When the subspace is expanded to 2×10 and 2×15, although some information is lost, the main deformation area can still be identified, and the efficiency is improved by about 80 times and 120 times , respectively.

Conclusions This paper provides a new approach to address the computational challenges in wide area long-term InSAR deformation monitoring, and has good engineering application prospects.