基于激光雷达和航拍影像的城市地物分类研究
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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。
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.
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.
引文
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[2] Yan W Y,Shaker A,El-Ashmawy N.Urban Land Cover Classification Using Airborne LiDAR Data:A Review[J].Remote Sensing of Environment,2015,158:295-310.
[3] Fernandez-Diaz J,Carter W,Glennie C,et al.Capability Assessment and Performance Metrics for The Titan Multispectral Mapping LiDAR[J].Remote Sensing,2016,8(11):936-970.
[4] Dong W,Lan J,Liang S,et al.Selection of LiDAR Geometric Features with Adaptive Neighborhood Size for Urban Land Cover Classification[J].International Journal of Applied Earth Observation and Geoinformation,2017,60:99-110.
[5] Yang B,Zhen D,Gang Z,et al.Hierarchical Extraction of Urban Objects From Mobile Laser Scanning Data[J].ISPRS Journal of Photogrammetry & Remote Sensing,2015,99:45-57.
[6] Shi Wenzhong.Principle of Modeling Uncertainties in Spatial Data and Analysis[M].Beijing:Science Press,2005.[史文中.空间数据与空间分析不确定性原理[M].北京:科学出版社,2005.]
[7] Zhang Jixian,Lin Xiangguo,Liang Xinlian.Advances and Prospects of Information Extraction from Point Clouds[J].Acta Geodaetica et Cartographica Sinica,2017,46(10):1460-1469.[张继贤,林祥国,梁欣廉.点云信息提取研究进展和展望[J].测绘学报,2017,46(10):1460-1469.]
[8] Sun Meng.Research on Classification Method of Land Cover by Fusing Aribone LiDAR Point Clouds and Aerial Imagery[D].Xuzhou:China University of Mining and Technology,2016.[孙蒙.机载LiDAR点云与航空影像融合的地物分类方法研究[D].徐州:中国矿业大学,2016.]
[9] Xu Chuanyang,Li Jianhong.Object-oriented Classification with High Resolution Image and LiDAR Data[J].Journal of Henan Polytechnic University,2015,34(2):222-225.[许传阳,李建红.高分辨率遥感影像结合LiDAR数据的面向对象分类方法[J].河南理工大学学报(自然科学版),2015,34(2):222-225.]
[10] Minh N Q,Hien L P.Land Cover Classification Using LiDAR Intensity Data and Neural Network[J].Journal of The Korean Society of Surveying,Geodesy,Photogrammetry and Cartography,2011,29(4):429-438.
[11] Dong Baogen,Ma Hongchao,Che Sen,et al.Method of Land Cover Refined Classification Supported by LiDAR Point Clouds[J].Remote Sensing Technology and Application,2016,31(1):165-169.[董保根,马洪超,车森,等.LiDAR点云支持下地物精细分类的实现方法[J].遥感技术与应用,2016,31(1):165-169.]
[12] Hu Bengang.Object-oriented Damage Building Extraction based on LiDAR and High Resolution Remote Sensing Imagery[D].Chengdu:Southwest Jiaotong University,2013.[胡本刚.基于LiDAR 点云与高分影像的面向对象的损毁建筑物提取方法研究[D].成都:西南交通大学,2013.]
[13] Bigdeli B,Samadzadegan F,Reinartz P.Fusion of Hyperspectral and LiDAR Data Using Decision Template-based Fuzzy Multiple Classifier System[J].International Journal of Applied Earth Observations & Geoinformation,2015,38:309-320.
[14] Zhang W,Li W,Zhang C,et al.Parcel-based Urban Land Use Classification in Megacity Using Airborne LiDAR,High Resolution Orthoimagery,and Google Street View[J].Computers,Environment and Urban Systems,2017,64:215-228.
[15] Sithole G,Vosselman G.Filtering of Airborne Laser Scanner Data based on Segmented Point Clouds[J].International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences,2005,36(3/w19):66-71.
[16] Wang H,Wang S,Chen Q,et al.An Improved Filter of Progressive TIN Densification for LiDAR Point Cloud Data[J].Wuhan University Journal of Natural Sciences,2015,20(4):362-368.
[17] Sun Bozhong.Multi-scale Segmentation Technique in High Resolution Image Information Extraction Application Research[D].Xi’an:Xi’an University of Science and Technology,2011.[孙波中.多尺度分割技术在高分辨率影像信息提取中的应用研究[D].西安:西安科技大学,2011.]
[18] Sun Ping,Deng Lei,Nie Juan.Multi-scale Remote Sensing Image Fusion Method based on Region Segmentation[J].Remote Sensing Technology and Application,2012,27(6):44-49.[孙萍,邓磊,聂娟.一种基于区域分割的多尺度遥感图像融合方法[J].遥感技术与应用,2012,27(6):844-849.]
[19] Feng Xin,Du Shihong,Zhang Fangli,et al.Urban Land Classification of High Resolution Images based on Multi-Scale Fusion[J].Geography and Geo-Information Science,2013,29(3):43-47.[冯昕,杜世宏,张方利,等.基于多尺度融合的高分辨率影像城市用地分类[J].地理与地理信息科学,2013,29(3):43-47.]
[20] He Shaolin,Xu Jinghua,Zhang Shuaiyi.Land Use Classification of Object-oriented Multi-scale by UAV Image[J].Remote Sensing for Land & Resources,2013,25(2):107-112.[何少林,徐京华,张帅毅.面向对象的多尺度无人机影像土地利用信息提取[J].国土资源遥感,2013,25(2):107-112.]
[21] Wang Huixian,Jin Huijia,Wang Jiaolong,et al.Optimization Approach for Multi-scale Segmentation of Remotely Sensed Imagery Under K-Means Clustering Guidance[J].Acta Geodaetica et Cartographica Sinica,2015,44(5):526-532.[王慧贤,靳惠佳,王娇龙,等.K均值聚类引导的遥感影像多尺度分割优化方法[J].测绘学报,2015,44(5):526-532.]
[22] Chen T,Guestrin C.XGBoost:A Scalable Tree Boosting System[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2016:785-794.
[23] Zhou Zhihua.Machine Learning[M].Beijing:Tsinghua University Press,2016.[周志华.机器学习[M].北京:清华大学出版社,2016.]
[24] Chen Tan.Research on Buildings Shadow Processing in High-Spatial Resolution RS Images[D].Chengdu:Southwest Jiaotong University,2014.[陈锬.高空间分辨率遥感影像中建筑物阴影的处理研究[D].成都:西南交通大学,2014.]