Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration
This work addresses the need for efficient and versatile DEM refinement in remote sensing and photogrammetry, offering a unified approach that improves over task-specific methods.
The paper tackles the problem of enhancing Digital Elevation Models (DEMs) by proposing a unified generative model for multipurpose restoration, achieving highly competitive performance on tasks like super-resolution, void filling, and denoising compared to state-of-the-art methods.
Digital Elevation Models (DEMs) are indispensable in the fields of remote sensing and photogrammetry, with their refinement and enhancement being critical for a diverse array of applications. Numerous methods have been developed for enhancing DEMs, but most of them concentrate on tackling specific tasks individually. This paper presents a unified generative model for multipurpose DEM restoration, diverging from the conventional approach that typically targets isolated tasks. We modify the mean-reverting stochastic differential equation, to generally refine the DEMs by conditioning on the learned terrain priors. The proposed Efficient Terrain Stochastic Differential Equation (ET-SDE) models DEM degradation through SDE progression and restores it via a simulated reversal process. Leveraging efficient submodules with lightweight channel attention, this adapted SDE boosts DEM quality and streamlines the training process. The experiments show that ET-SDE achieves highly competitive restoration performance on super-resolution, void filling, denoising, and their combinations, compared to the state-of-the-art work. In addition to its restoration capabilities, ET-SDE also demonstrates faster inference speeds and the capacity to generalize across various tasks, particularly for larger patches of DEMs.