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A Simple Deep Learning Network for classification of 3D Mobile lidar Point Clouds

Journal of Geodesy and Geoinformation Science 2021年第3期4卷 49-59页

作者： Yanjun WANG Shaochun LI Mengjie WANG Yunhao LIN Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtan 411201China National-local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtan 411201China School of Resource Environment and Safety Engineering Hunan University of Science and TechnologyXiangtan 411201China

Automatic and accurate classification is a fundamental problem to the analysis and modeling of lidar(Light Detection and Ranging)***,convolutional neural network(ConvNet or CNN)has achieved remarkable performance in image recognition and computer *** significant efforts have also been made to develop various deep networks for satellite image scene classification,it still needs to further investigate suitable deep learning network frameworks for 3D dense mobile laser scanning(MLS)*** this paper,we present a simple deep CNN for multiple object classification based on multi-scale context *** the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each ***,the classification task can be treated as the image recognition using *** proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit(ReLU)layers,which combined multiple deeper network *** being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine *** with the widely used ResNet algorithm,this model performs better precision and recall rates,and less processing time,which indicated the significant potential of deep-learning-based methods in MLS data classification.

关键词： deep learning convolutional neural network(CNN) mobile laser scanning(MLS) lidar data classification point-to-image transformation

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