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遥感技术与应用  2010, Vol. 25 Issue (3): 323-327    DOI: 10.11873/j.issn.1004-0323.2010.3.323
研究与应用     
基于建筑物提取的精细尺度人口估算研究

冯甜甜,龚健雅
武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079
Investigation on Small-area Population Estimation Based on Building Extraction
FENG Tian-tian,GONG Jia-|ya
State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan 430079,China
 全文: PDF(1124 KB)  
摘要:

以精细尺度的人口估算为目标,提出一种根据居民区建筑物属性估算人口数量的方法。首先基于Dempster-Shafer证据理论,结合LiDAR数据和高分辨率遥感影像进行建筑物的自动提取。根据土地利用分类图排除提取结果中的非居民区建筑后,按照线性回归的思想,通过对居民建筑物的数量、面积、体积等几何属性的优化选择建立人口估算模型。实验表明,利用该估算模型能够获得较高精度的小面积目标区域上的估算结果。该方法提高了人口估算的精细程度和自动化程度。

关键词: 建筑物提取人口估算小面积区域Dempster-Shafer理论线性回归分析    
Abstract:

In order to estimate the population of small area,a new population estimation method based on residential building attributes is presented.Firstly,buildings are extracted combining LiDAR data with high-resolution remote sensing images,using Dempster-Shafer theory of evidence.Then,all the non-residential buildings are excluded from extraction results according to land use classification map,and the population estimation model is generated by optimization choice of geometric attributes of residential buildings,such as building counts,building areas,and building volumes,based on linear regression method.The results demonstrated that proposed model yielded good small\|area population estimation results,and the proposed method improved the precision and automatic of population estimation.

Key words: Building extraction    Population estimation    Small-area    Dempster-Shafer theory    Linear regression analysis
出版日期: 2010-10-20
基金资助:

国家自然科学基金创新研究群体资助项目(40721001)及国家973计划资助项目(2006CB701304)资助。

作者简介: 冯甜甜(1983-),女,博士研究生,主要从事建筑物自动提取、精细尺度的人口估算研究。E-mail:fancyftt@gmail.com。
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引用本文:

冯甜甜, 龚健雅. 基于建筑物提取的精细尺度人口估算研究[J]. 遥感技术与应用, 2010, 25(3): 323-327.

FENG Tian-tian, GONG Jian-ya. Investigation on Small-area Population Estimation Based on Building Extraction. Remote Sensing Technology and Application, 2010, 25(3): 323-327.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2010.3.323        http://www.rsta.ac.cn/CN/Y2010/V25/I3/323

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