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arXiv · 2407.01908

Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration

Tongtong Zhang·Zongcheng Zuo·Yuanxiang Li
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Abstract

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.

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Tongtong Zhang, Zongcheng Zuo, Yuanxiang Li. 2024-11-24. Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration. https://arxiv.org/abs/2407.01908

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