MicroFlow: Domain-Specific Optical Flow for Ground Deformation Estimation in Seismic Events
This work addresses the challenge of dense ground displacement measurement for geological studies, offering an incremental improvement over traditional methods with better accuracy in real-world scenarios.
The paper tackled the problem of estimating ground deformation from satellite images by proposing a domain-specific optical flow model that achieves sub-pixel precision and preserves fault-line sharpness, significantly outperforming existing geophysics methods on benchmarks.
Dense ground displacement measurements are crucial for geological studies but are impractical to collect directly. Traditionally, displacement fields are estimated using patch matching on optical satellite images from different acquisition times. While deep learning-based optical flow models are promising, their adoption in ground deformation analysis is hindered by challenges such as the absence of real ground truth, the need for sub-pixel precision, and temporal variations due to geological or anthropogenic changes. In particular, we identify that deep learning models relying on explicit correlation layers struggle at estimating small displacements in real-world conditions. Instead, we propose a model that employs iterative refinements with explicit warping layers and a correlation-independent backbone, enabling sub-pixel precision. Additionally, a non-convex variant of Total Variation regularization preserves fault-line sharpness while maintaining smoothness elsewhere. Our model significantly outperforms widely used geophysics methods on semi-synthetic benchmarks and generalizes well to challenging real-world scenarios captured by both medium- and high-resolution sensors. Project page: https://jbertrand89.github.io/microflow/.