用单幅SAR影像实现跨城市建筑高度精准估算，突破了以往模型在异域场景的局限。
An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images
- 基于目标框检测与回归的分步方法，从单张高分辨率SAR图像中提取建筑高度
- 在慕尼黑达到2.20米均方绝对误差，相当于约一层楼高，优于现有最先进方法
- 首次在多洲城市数据集上验证跨大陆泛化能力，适合城市规划与遥感应用
利用极高分辨率合成孔径雷达（VHR SAR）图像精确估计建筑高度对城市应用至关重要。本文提出一种基于深度学习的自动化方法，通过目标框检测后接高度回归，从单幅VHR COSMO-SkyMed影像中估算建筑高度。模型在涵盖欧洲、北美、南美和亚洲八座地理多样城市的独特多洲数据集上训练与评估，并采用交叉验证策略显式检验分布外（OOD）泛化性能。结果显示，在欧洲城市表现优异，慕尼黑地区均方绝对误差达2.20米（约一层楼高），显著超越同类场景下最新先进方法。尽管在其他大陆尤其是亚洲城市因城市形态差异和高层建筑普遍而误差增加，但本研究证实深度学习在单幅VHR SAR数据上实现跨城市、跨大陆迁移学习具有巨大潜力。
原文摘要 · Abstract (English)
Accurate estimation of building heights using very high resolution (VHR) synthetic aperture radar (SAR) imagery is crucial for various urban applications. This paper introduces a Deep Learning (DL)-based methodology for automated building height estimation from single VHR COSMO-SkyMed images: an object-based regression approach based on bounding box detection followed by height estimation. This model was trained and evaluated on a unique multi-continental dataset comprising eight geographically diverse cities across Europe, North and South America, and Asia, employing a cross-validation strategy to explicitly assess out-of-distribution (OOD) generalization. The results demonstrate highly promising performance, particularly on European cities where the model achieves a Mean Absolute Error (MAE) of approximately one building story (2.20 m in Munich), significantly outperforming recent state-of-the-art methods in similar OOD scenarios. Despite the increased variability observed when generalizing to cities in other continents, particularly in Asia with its distinct urban typologies and prevalence of high-rise structures, this study underscores the significant potential of DL for robust cross-city and cross-continental transfer learning in building height estimation from single VHR SAR data.
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