基于改进层次分析的机载LiDAR点云分类
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摘要
针对机载LiDAR点云信息挖掘研究,提出了改进层次分析的机载LiDAR点云分类方法。首先选出5个判定准则,使用层次分析方法对每一个点云做出相应的判定,然后根据一定的规则对判定的结果生成一个二进制信号,最后使用BP神经网络方法确定判定因子在不同地物分类中的权重并对机载LiDAR点云分类。实验数据结果表明:该方法能够很好的将点云数据分类为高大的树、人工建筑物、低矮植物、地表和道路,分类精度Kappa系数达到0.87。
For the study on information mining of airborne LiDAR point cloud,this paper proposes airborne LiDAR point cloud classification based on improved analytic hierarchy process(AHP).Firstly,five criteria are selected to decide each point cloud by means of improved analytic hierarchy process,and then a binary signal is generated for the results according to certain rules.Finally,the weights of the decision factors in different ground objects are determined by BP neural network method and the airborne LiDAR point clouds are classified.The experimental results show that this method can well classify the point cloud data into tall trees,artificial buildings,low plants,land surface and roads,with classification accuracy Kappa coefficient up to 0.87.
For the study on information mining of airborne LiDAR point cloud,this paper proposes airborne LiDAR point cloud classification based on improved analytic hierarchy process(AHP).Firstly,five criteria are selected to decide each point cloud by means of improved analytic hierarchy process,and then a binary signal is generated for the results according to certain rules.Finally,the weights of the decision factors in different ground objects are determined by BP neural network method and the airborne LiDAR point clouds are classified.The experimental results show that this method can well classify the point cloud data into tall trees,artificial buildings,low plants,land surface and roads,with classification accuracy Kappa coefficient up to 0.87.
引文
[1]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.
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[10]乔纪纲,刘小平,张亦汉.基于LiDAR高度纹理和神经网络的地物分类[J].遥感学报,2011,15(3):539-553.
[11]潘锁艳,管海燕.基机载多光谱LiDAR数据的地物分类方法[J].测绘学报,2018,47(2):198-207.
[12]Zhang W,Qi J,Wan P,et al.An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation[J].Remote Sensing,2016,8(6):501.
[13]梁宇乾,袁希平,甘淑.基于LiDAR点云回光强度信息的KNN算法与BP神经网络算法分类对比研究[J].软件导刊,2018,17(4):77-81.
[14]马锐.人工神经网络原理[M].北京:机械工业出版社,2014.
[2]熊艳,高仁强,徐战亚.机载LiDAR点云数据降维与分类的随机森林方法[J].测绘学报,2018,47(4):508-518.
[3]林祥国,宗浩.基于语义推理的城区机载LiDAR分割点云分类[J].测绘科学,2014,39(1):38-44.
[4]杨俊涛.基于概率图模型的机载LiDAR点云数据分类[D].北京:中国地质大学(北京),2017.
[5]Wang Hongzhou,Glennie C.Fusion of Waveform LiDAR Data and Hyperspectral Imagery for Land Cover Classification[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,108:1-11.
[6]董保根.机载LiDAR点云与遥感影像融合的地物分类技术研究[D].郑州:解放军信息工程大学,2013.
[7]Awrangjeb M,Zhang C,Fraser C S.Automatic Extraction of Building Roofs Using LiDAR Data and Multispectralimagery[J].ISPRS Journal of Photogrammetry and Remote Sensing,2013,83:1-18.
[8]方军.融合LiDAR数据和高分辨率遥感影像的地物分类方法研究[D].武汉:武汉大学,2014.
[9]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]乔纪纲,刘小平,张亦汉.基于LiDAR高度纹理和神经网络的地物分类[J].遥感学报,2011,15(3):539-553.
[11]潘锁艳,管海燕.基机载多光谱LiDAR数据的地物分类方法[J].测绘学报,2018,47(2):198-207.
[12]Zhang W,Qi J,Wan P,et al.An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation[J].Remote Sensing,2016,8(6):501.
[13]梁宇乾,袁希平,甘淑.基于LiDAR点云回光强度信息的KNN算法与BP神经网络算法分类对比研究[J].软件导刊,2018,17(4):77-81.
[14]马锐.人工神经网络原理[M].北京:机械工业出版社,2014.