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基于Google Earth遥感影像的城市建筑物高度反演 被引量:5

Inversion of urban buildings height based on Google Earth remote sensing images
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摘要 基于光学遥感影像中建筑物的阴影分布,推导了建筑物高度与阴影分布的关系函数,在此基础上构建了建筑物高度反演方法,并在北京市典型区进行了实例研究。结果表明:利用建筑物在夏季的阴影全长或在冬季可视部分的阴影长提取高度,分别有90.9%和84.8%的建筑物高度误差控制在实测值的5%以内;在提取居民区或高层建筑物的高度时应尽量采用夏季的遥感影像,减少阴影遮挡的情况;低矮建筑物的高度提取,宜采用阴影更为显著的冬季影像可视阴影部分进行反演计算;在缺乏卫星轨道等影像参数时,利用典型已知建筑的高度反推出相关参数,并推求同景影像中其他建筑物高度的方法是可行的,为利用城市代表性建筑物推求建筑群高度提供了可能。 Based on the distribution of building shadows in optical remote sensing images,the relationship between building height and shadow distribution is derived.Based on this,the buildings height inversion method is established,and the height information inversion of the typical buildings in Beijing is realized.The results show that the height of the building is extracted using the full length of the shadow of the building in summer or the length of the visible part in the winter,90.9%and 84.8%of building calculation errors are controlled within 5%of the height of the building.When extracting the height of residential areas or high-rise buildings,summer remote sensing images should be used as much as possible to reduce the shadow occlusion.When the height of the low building is extracted,it is better to use the shaded part of the winter image to perform the inversion calculation.In the absence of image parameters such as satellite orbit,use the height of the typical known building to reverse the relevant parameters,and it is feasible to use those parameters to estimate the height of other buildings in the same scene image,providing the possible of using the typical urban buildings to estimate the height of buildings.
作者 陈冲 杨志勇 史晓亮 尚雨 CHEN Chong;YANG Zhiyong;SHI Xiaoliang;SHANG Yu(Xi’an University of Science and Technology,Xi’an 710054,China;China Institute of Water Resources and Hydropower Research,Beijing 100038,China)
出处 《测绘通报》 CSCD 北大核心 2020年第1期98-101,共4页 Bulletin of Surveying and Mapping
基金 国家自然科学基金(51879274,51739011).
关键词 建筑物高度 阴影提取 遥感反演 目视解译 特征线 building height shadow extraction remote sensing inversion visual interpretation feature line
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