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遥感技术与应用  2019, Vol. 34 Issue (2): 253-262    DOI: 10.11873/j.issn.1004-0323.2019.2.0253
LiDAR专栏     
基于激光雷达和航拍影像的城市地物分类研究
徐凡,张雪红,石玉立
(南京信息工程大学遥感与测绘工程学院,江苏 南京 210044)
Research on Classification of Land Cover based on LiDAR Cloud and Aerial Images
Xu Fan,Zhang Xuehong,Shi Yuli
(School of Remote Rensing & Geomatics Engineering,Nanjing University of Information  Science &Technology,Nanjing 210044,China)
 全文: PDF(15572 KB)  
摘要: 航拍影像富含光谱信息、纹理信息和空间信息,机载LiDAR(Light Detection and Ranging)能够提供地物的三维信息。综合利用两类数据的优势,研究了一种面向对象的城市地物分类方法。通过预处理将LiDAR点云转换成二维栅格数据,与航拍影像进行配准;结合光谱信息和高度信息对研究影像进行多尺度分割,依据最优分割尺度计算模型选择最优分割尺度;对分割对象提取各类特征,采用XGBoost算法进行特征选择,选择支持向量机(Support Vector Machine,SVM)分类器进行分类,为体现XGBoost算法的优势,借助SVM分类器与Relief和RFE两种传统的特征选择算法比较;基于一定规则将阴影区域地物区分以及合并到真实地物类别中,实现最终的城市地物分类。在3个区域测试分类方法,结果表明本文研究方法可行有效,能够较好地应用于城市地物分类。
关键词: 激光雷达航拍影像面向对象分类XGBoostSVM
    
Abstract: Aerial images contain abundant spectral information,texture information and spatial information,and airborne LiDAR can provide three-dimensional information of ground objects.An object-oriented classification method was researched by taking advantages of the two types of data.Converting LiDAR point cloud into 2-D raster image by preprocessing,and matched it with aerial image.Then,multi-scale segmentation algorithm was applied to image segmentation based on spectral information and height information.Next,XGBoost algorithm were applied to select features extracted from segmented object respectively.The SVM classifier was used to classify and prove the superiority of XGBoost algorithm by comparing with two traditional feature selection algorithms:Relief and RFE.Finally,objects at shadow regions were distinguished and merged into real objects based on certain rules.Testing the method in three regions,the results showed that the method was feasible and effective,and could be well applied to the classification of urban ground object.
Key words: LiDAR    Aerial imagery    Object-oriented classification    XGBoost    SVM
收稿日期: 2018-09-22 出版日期: 2019-05-10
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目“异速增长和资源限制模型结合多源遥感数据估算森林地上生物量研究”(41471312),国家自然科学基金项目“反射率与叶绿素荧光遥感协同的冬小麦条锈病早期诊断研究”(41871239),中国博士后科学基金项目“协同反射率与叶绿素荧光的冬小麦水分胁迫早期探测研究”(2017M610338),河北省气象与生态环境重点实验室开放研究基金项目“基于机载高光谱数据的冬小麦水分胁迫探测研究”(Z201607Y),河北省创新能力提升计划项目(18964201H)。
作者简介: 徐凡(1994-),男,江苏扬中人,硕士,主要从事遥感影像与LiDAR结合分类及机器学习方面的研究。E-mail:173462553@qq.com。
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引用本文:

徐凡, 张雪红, 石玉立. 基于激光雷达和航拍影像的城市地物分类研究[J]. 遥感技术与应用, 2019, 34(2): 253-262.

Xu Fan, Zhang Xuehong, Shi Yuli. Research on Classification of Land Cover based on LiDAR Cloud and Aerial Images. Remote Sensing Technology and Application, 2019, 34(2): 253-262.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.2.0253        http://www.rsta.ac.cn/CN/Y2019/V34/I2/253

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