WaveDINO: Learning-Based Atmospheric Correction of Unwrapped InSAR Interferograms Validated by GNSS: Results at Laguna del Maule and Campi Flegrei Volcanoes
2026-06-15 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new method called WaveDINO to clean up noisy images from a radar technique used to monitor volcanoes. Instead of relying only on weather models, they trained their system using a mix of simulated volcanic movements and real atmospheric noise. Their method looks at the data in different layers and uses knowledge of the land's shape to better remove errors. Tests on real volcano data showed WaveDINO matched ground measurements more closely and worked better than existing correction methods. This helps make volcanic monitoring more accurate by reducing false signals caused by the atmosphere.
Interferometric Synthetic Aperture Radar (InSAR)Volcanic deformationAtmospheric phase delaysSynthetic deformationWavelet denoisingDINOv3 foundation modelTerrain informationGNSS measurementsNumerical weather modelsDecorrelation effects
Authors
Robert Popescu, Juliet Biggs, Tianyuan Zhu, Nantheera Anantrasirichai
Abstract
Interferometric Synthetic Aperture Radar (InSAR) enables effective monitoring of volcanic deformation; however, the observed signals are often corrupted by atmospheric phase delays, seasonal surface changes, and decorrelation effects. Existing atmospheric correction methods, such as numerical weather model-based methods, can reduce these effects but do not consistently remove atmospheric artefacts and may introduce residual biases. To address these limitations, we propose a novel learning-based method for denoising unwrapped InSAR interferograms, using a hybrid training strategy that combines physically motivated synthetic deformation with real atmospheric noise. Specifically, we introduce WaveDINO, a wavelet-based multi-scale denoising framework conditioned on frozen DINOv3 foundation-model features and terrain information. Training uses synthetic magma-source deformation superimposed on short-term interferograms to expose the network to realistic atmospheric statistics while retaining known ground truth. Performance is evaluated on both controlled synthetic data and long-term real interferograms from Laguna del Maule (Chile) and Campi Flegrei (Italy), with independent GNSS measurements used for validation. WaveDINO consistently outperforms competing models, improving agreement with GNSS measurements, and reducing mean GNSS misfit by approximately 3% and 19% at two sites, respectively, while surpassing weather-model-based corrections.