摘要
近年来通过提取建筑物阴影计算其高度有较多的研究,但这些方法对于密集建筑群或复杂地面等情况较难实现。针对这一问题,可以用建筑物侧面信息反演建筑物高的方式弥补其缺陷。文章分别采用Otsu算法和LVQ(learning vector quantization)神经网络提取实验区高分遥感影像中的建筑物侧面信息,并计算其长度,然后根据成像物理模型计算建筑物的高度。通过与实测建筑物的高度进行对比,表明该方法可以满足建筑物高度计算的精度要求,并且可以与阴影提取方法的结果互补,最终可获取研究区域绝大多数建筑物的高度信息。
In recent years,there are many researches on calculating the height of buildings by extracting the shadow of buildings,but this method is difficult to be realized in the case of dense buildings or complex ground.To solve this problem,we can use the side information of the building to retrieve the height of the building.In this paper,Otsu algorithm and LVQ neural network are used to extract the side information of buildings from high-resolution remote sensing images.By calculating its length,we can then calculate the height of the building according to the physical model of imaging.Compared with the measured building height,the results of this method can meet the precision requirements,the method can complement the results of shadow extraction method,and the height information of most buildings in the area can be obtained.
作者
唐昊
冯德俊
曹文峰
德云乐强
王淳
TANG Hao;FENG Dejun;CAO Wenfeng;DE Yunleqiang;WANG Chun(Institute of Computer Application,China Academy of Engineering Physics,Mianyang,Sichuan 621900,China;Faculty of Geosciences and Environmental Engineering,Southwest Jiaotong University,Chengdu 610097,China)
出处
《遥感信息》
CSCD
北大核心
2021年第4期63-67,共5页
Remote Sensing Information
基金
科技基础资源调查专项(2019FY202504)。
关键词
侧面信息提取
建筑物高度估算
高分遥感影像
深度学习
建筑物阴影
side information extraction
building height estimation
high resolution remote sensing image
deep learning
shadow of building
