Interferometric Synthetic Aperture Radar

Interferometric Synthetic Aperture Radar (InSAR) uses radar signals to create high-resolution images of ground deformation, enabling monitoring of diverse phenomena like earthquakes, volcanic activity, and land subsidence. Current research emphasizes developing advanced machine learning models, including Bayesian networks, diffusion models, and complex-valued convolutional neural networks, to improve the accuracy and efficiency of extracting information from often noisy InSAR data, particularly for tasks like building damage assessment and soil moisture estimation. This is driven by the need for rapid and accurate hazard assessment in disaster response and improved understanding of Earth's surface processes, with applications ranging from infrastructure management to agricultural monitoring. The development of large, annotated InSAR datasets and self-supervised learning techniques are also key areas of focus to overcome data limitations and improve model generalizability.