A Semi-Supervised Self-Boosting Learning Method for InSAR Phase Denoising

Interferogram denoising is critical in interferometric synthetic aperture radar (InSAR) data processing, directly impacting the accuracy and reliability of final products. Current data-driven denoising models rely on training samples with noises simulated from certain statistic models. However, such simulations may not fully capture the complexity of real-world data, resulting in degraded performance in practical applications. Here, we introduce a learning method for InSAR phase denoising, marking the first instance of training a model with real-world noise. Our method consists of two phases: (1) model excitation, where the model is initially trained with simulated noise to develop basic denoising capabilities; (2) refinement boosting, an unsupervised, iterative process where the model gradually refines itself using real-world noise through noise extraction, noise purification, data augmentation, and model enhancement. We identified the cost-optimal denoising model by conducting experiments across network architectures of varying complexity, using identical training datasets and experimental settings. Experiments on synthetic interferograms with varying coherence levels show that our method achieves the lowest denoising errors, outperforming state-of-the-art methods such as NL-InSAR and InSAR-BM3D, while maintaining fast inference times comparable to the BoxCar method. Applied to real Sentinel-1 interferograms, our approach demonstrates superior denoising performance, as evidenced by the fewest residues and the smoothest unwrapping results. By analyzing noises simulated from the coherence-guided statistical model and extracted from Sentinel-1 interferograms, we find significant discrepancies between simulated and real-world noise, underscoring the importance of preparing training samples with real-world noise for InSAR data-driven models, e.g., denoising, unwrapping, and other applications.