Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling

• URL: http://arxiv.org/abs/2501.15440v2
• Date: Fri, 31 Jan 2025 10:18:03 GMT
• Title: Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling
• Authors: Daniel Panangian, Ksenia Bittner,
• Abstract summary: Digital Surface Models (DSMs) are essential for accurately representing Earth's topography in geospatial analyses.<n>DSMs capture detailed elevations of natural and manmade features, crucial for applications like urban planning, vegetation studies, and 3D reconstruction.<n>Previous studies have primarily focused on void filling for digital elevation models (DEMs) and Digital Terrain Models (DTMs)<n>We introduce Dfilled, a guided DSM void filling method that leverages optical remote sensing images through edge-enhancing diffusion.
• Score: 2.3020018305241337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital Surface Models (DSMs) are essential for accurately representing Earth's topography in geospatial analyses. DSMs capture detailed elevations of natural and manmade features, crucial for applications like urban planning, vegetation studies, and 3D reconstruction. However, DSMs derived from stereo satellite imagery often contain voids or missing data due to occlusions, shadows, and lowsignal areas. Previous studies have primarily focused on void filling for digital elevation models (DEMs) and Digital Terrain Models (DTMs), employing methods such as inverse distance weighting (IDW), kriging, and spline interpolation. While effective for simpler terrains, these approaches often fail to handle the intricate structures present in DSMs. To overcome these limitations, we introduce Dfilled, a guided DSM void filling method that leverages optical remote sensing images through edge-enhancing diffusion. Dfilled repurposes deep anisotropic diffusion models, which originally designed for super-resolution tasks, to inpaint DSMs. Additionally, we utilize Perlin noise to create inpainting masks that mimic natural void patterns in DSMs. Experimental evaluations demonstrate that Dfilled surpasses traditional interpolation methods and deep learning approaches in DSM void filling tasks. Both quantitative and qualitative assessments highlight the method's ability to manage complex features and deliver accurate, visually coherent results.

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