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2024年5月15日,Elsevier 旗下top期刊《International Journal Of Applied Earth Observation And Geoinformation》在线发表了云南师范大学地理学部程峰博士最新研究成果《Urban building height extraction accommodating various terrain scenes using ICESat-2/ATLAS data》。云南师范大学地理学部程峰博士为同学作者。合作单位有石家庄大学Junliang He、云南大学Jieying Lao 、中国科学院空天信息创新研究院王成等单位。
Abstract
Although the photon point cloud data acquired from ICESat-2/ATLAS can be efficiently employed in urban building height extraction, its universal applicability in undulating terrain scenarios is constrained, and there are noticeable issues of false positives and false negatives. This research establishes a terrain-adaptive methodological framework based on ICESat-2/ATLAS photon point cloud to extract high-precision, high-density building height data across varied urban topographical conditions. First, a terrain-adaptive elevation buffer is utilized to coarse denoise the photon point cloud, involving the removal of the majority of noise photons in the scene, thereby enhancing the efficiency of subsequent algorithms. Second, urban signal photons are extracted from the remaining original photons using the Adaptive Method Based on Single-Photon Spatial Distribution (SPSD-AM). This approach demonstrates high universality across various urban scenes, while simultaneously ensuring a stable accuracy of urban signal photon extraction. Subsequently, ground photons are extracted from the urban signal photons and fit the ground curve based on the Adaptive Method Based on Spatial Differences of Urban Signal Photons (USPSD-AM), which addresses the challenge of the potential mixing of ground and building photons in complex terrain scenarios. A precise ground curve is then employed to extract building photons from urban signal photons. In order to mitigate issues such as false positives and negatives, post-processing steps, including completion and denoising of building photons, are implemented. Finally, the acquired building photons and ground curve are adopted to extract accurate building height parameters. The precision verification results show that the extracted building heights are considerably consistent with the reference building heights. The mean RMSE and MAE are 0.273 m and 0.202 m for flat terrains and 1.168 m and 0.759 m for undulating terrains, respectively. The proposed method demonstrates superior applicability across diverse urban scenarios, providing a robust theoretical foundation for large-scale urban building height retrieval efforts.
扩展阅读:
姓 名:程峰 |
最高学历:博士 |
职 称:讲师/硕士生导师 |
工作单位:云南师范大学地理学部 |
电子邮箱:chengfeng_rs@163.com 教育及工作履历 2018.03—现在,云南师范大学,地理学部,讲师。 2014.09—2017.12,南京师范大学,地理科学学院,地理环境遥感,博士; 2008.09—2011.07,云南师范大学,旅游与地理科学学院,地图学与地理信息系统,硕士; 2004.09—2008.07,海南师范大学, 地理与环境科学学院,地理科学,学士。 基金项目 [1] 国家自然科学基金项目,融合主被动遥感的复杂山地森林地上生物量估算研究(2024-2027年),项目编号:42361065,项目负责人:程峰; [2] 云南省应用基础研究计划青年项目,滇西北三江并流区草地退化遥感监测研究(2020-2023年),项目编号:202001AU070060,项目负责人:程峰; [3] 云南省教育厅科学研究基金项目,基于PARASOL遥感数据的大气气溶胶参数反演算法研究,项目编号:2019J0071,项目负责人:程峰。 代表性论文: |
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