摘要
建筑物区域的精确提取是数字地形图自动化绘制的基础。本文通过融合无人机航摄处理得到的高分辨率DSM(digital surface model)与DOM(digital orthophoto map)数据,提出一种基于阈值分割、SVM(support vector machines)分类和形态学处理的建筑物区域提取算法,并选取ISPRS提供的Potsdam数据集进行试验,通过定量化精度分析验证了该算法的可行性及可靠性。
The accurate extraction of building area provides the base for automatic drawing of digital topographic map.This paperproposes an algorithm of building area extraction,which is based on threshold segmentation,SVM classification and morphology processing by combining high-resolution DSM and DOM data obtained by UAV aerial photography.The Potsdam data set provided by ISPRS is selected for experiment,and the feasibility and reliability of the proposed algorithm are verified by quantitative accuracy analysis.
作者
丁育萱
邹进贵
朱晓康
DING Yuxuan;ZOU Jingui;ZHU Xiaokang(School of Geodesyand Geomatics,Wuhan University,Wuhan 430079,China;Key Laboratory of Precise Engineering and Industry,National Administration of Surveying,Mapping and Geo-information,Wuhan University,Wuhan 430079,China;State Grid General Aviation Co.,Ltd.,Beijing 102209,China)
出处
《测绘地理信息》
CSCD
2021年第6期86-89,共4页
Journal of Geomatics
基金
国家自然科学基金(41871373)。