Automatic building rooftop extraction using a digital surface model derived from aerial stereo images

被引：15

作者：

Wu, Bin ^{[1
,2
]}

Wu, Siyuan ^{[1
,2
]}

Li, Yong ^{[1
,2
]}

Wu, Jianping ^{[1
,2
]}

Huang, Yan ^{[1
,2
]}

Chen, Zuoqi ^{[3
,4
]}

Yu, Bailang ^{[1
,2
]}

机构：

[1] East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai, Peoples R China

[2] East China Normal Univ, Sch Geog Sci, Shanghai, Peoples R China

[3] Fuzhou Univ, Natl & Local Joint Engn Res Ctr Satellite Geospat, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China

[4] Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China

来源：

JOURNAL OF SPATIAL SCIENCE | 2022年 / 67卷 / 01期

基金：

国家重点研发计划; 中国博士后科学基金; 中国国家自然科学基金;

关键词：

Building rooftop; digital surface model; large urban area; dilation reconstruction; region growing and merging; AIRBORNE LIDAR DATA; RESOLUTION SATELLITE IMAGERY; SOLAR-RADIATION; SEGMENTATION; RECONSTRUCTION; DSM; INFORMATION; MORPHOLOGY; REGION;

D O I：

10.1080/14498596.2020.1720836

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

Automatic building rooftop extraction is of great importance to many applications including building reconstruction, solar energy supply, and disaster management. This study proposes a building rooftop extraction method using DSM data generated from aerial stereo images and vegetation cover vector data. The method consists of five steps: noise filtering, dilation reconstruction, vegetation and terrain region removal, region growing and merging, and post-processing. We applied the proposed method to the centre of Shanghai, China, a typical urban area. Experimental results show that the proposed method can successfully extract building rooftops, with an approximately 82.6% quality percentage and 96.2% matched overlay.

引用

页码：21 / 40

页数：20

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