Dfilled：重新利用边缘增强扩散进行引导式DSM空洞填充
Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling
AI总结：
针对DSM空洞填充问题，提出Dfilled方法，利用边缘增强扩散和Perlin噪声掩码，重新利用深度各向异性扩散模型进行修复，在复杂特征处理上超越传统插值和深度学习方法。
摘要：
数字表面模型（DSM）在地理空间分析中对于准确表示地球地形至关重要。DSM捕获自然和人造特征的详细高程，对城市规划、植被研究和3D重建等应用至关重要。然而，由于遮挡、阴影和低信号区域，从立体卫星图像导出的DSM通常包含空洞或缺失数据。先前的研究主要集中在数字高程模型（DEM）和数字地形模型（DTM）的空洞填充上，采用反距离加权（IDW）、克里金和样条插值等方法。尽管这些方法对简单地形有效，但往往无法处理DSM中存在的复杂结构。为克服这些局限性，我们引入了Dfilled，这是一种通过边缘增强扩散利用光学遥感图像的引导式DSM空洞填充方法。Dfilled重新利用最初为超分辨率任务设计的深度各向异性扩散模型来修复DSM。此外，我们利用Perlin噪声创建修复掩码，以模拟DSM中的自然空洞模式。实验评估表明，Dfilled在DSM空洞填充任务中超越了传统插值方法和深度学习方法。定量和定性评估均突显了该方法管理复杂特征并提供准确、视觉连贯结果的能力。
英文摘要：
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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