摘要
为了更好地进行城市建设和规划,对建筑物进行有效识别非常重要。针对目前遥感技术对建筑物难以实现高精度提取的问题,本文提出一种基于引入红色边缘波段规则的面向对象和基于样本的面向对象相结合的方法,提取城市建筑物信息。该方法利用worldview 2影像的全色和多光谱的融合数据,进行尺度分割,根据建筑物的光谱特征、形状特征、数字表面模型(digital surface model,DSM)和worldview2的红色边缘波段(RedEdge)的纹理特征建立双层规则知识库,进行建筑物信息提取;同时,利用基于样本的面向对象方法对worldview 2数据影像进行建筑物信息提取。最后,对2种方法获取的建筑物信息结果进行融合,实现建筑物的高精度提取。以广州市天河区试验区为例,研究结果表明:基于样本的面向对象法、基于规则的面向对象法、基于引入红色边缘波段规则的面向对象法以及本文方法的分类精度分别为81.27%,83.75%,87.06%,91.43%。基于引入红色边缘波段规则的面向对象与基于样本的面向对象分类相结合的方法比其他3种方法提取的精度都高,为高分辨率遥感影像建筑物信息的识别提供了有效的手段。
It is important to identify buildings effectively to better carry out urban construction and planning.To solve the problem that current research techniques are difficult to achieve high-precision extraction of buildings,a method based on the combination of object-oriented and RedEdge band rule and sample-based object-oriented was proposed to extract urban building information.Firstly,the fusion data of panchromatic and multispectral of worldview 2 image was adopted to segment the scale,a double-layer rule knowledge base was built to extract the building information according to the spectral characteristics and shape characteristics of buildings,digital surface model(DSM)and texture features of RedEdge band of worldview 2 image.Secondly,the sample-based object-oriented method was used to extract building information from the worldview2 image.Finally,the results of building information obtained by the two object-oriented methods were fused to perform high-precision extraction of buildings.Taking Tianhe district of Guangzhou as an example,the results showed that the classification accuracy of sample-based object-oriented method,rule-based object-oriented method,object-oriented method based on RedEdge band rule and the proposed method were 81.27%,83.75%,87.06%and 91.43%,respectively.The results showed the method of combining object-oriented classification based on RedEdge band rule and sample-oriented classification was more accuracy than the other three methods,which provided an effective means for building information recognition of high-resolution remote sensing image.
作者
贺晓璐
刘振华
胡月明
HE Xiaolu;LIU Zhenhua;HU Yueming(The College of Natural Resources and Environment,South China Agricultural University,Guangzhou 510642,Guangdong,China;Key Laboratory for Redevelopment of Construction Land,Ministry of Land and Resources,Guangzhou 510642,Guangdong,China;Guangdong Provincial Key Laboratory of Land Use and Consolidation,Guangzhou 510642,Guangdong,China;Guangdong Province Engineering Research Center for Land Information Technology,Guangzhou 510642,Guangdong,China;College of Agriculture and Animal Husbandry,Qinghai University,Xining 810016,Qinghai,China;School of Resources and Environment,University of Electronic Science and Technology of China,Chengdu 610054,Sichuan,China)
出处
《河南理工大学学报（自然科学版）》
CAS
北大核心
2020年第2期53-61,共9页
Journal of Henan Polytechnic University（Natural Science）
基金
国家重点研发计划项目(2018YFD1100800)
广州市科技计划项目(201807010048)
青海省科技计划项目(2017-ZJ-730)
