A Simple Deep Learning Network for Classification of 3D Mobile LiDAR Point Clouds

Automatic and accurate classification is a fundamental problem to the analysis and modeling of Li DAR( Light Detection and Ranging) data. Recently,convolutional neural network( Conv Net or CNN) has achieved remarkable performance in image recognition and computer vision. While 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 3 D dense mobile laser scanning( MLS) data. In this paper,we present a simple deep CNN for multiple object classification based on multiscale context representation. For the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each point. Then,the classification task can be treated as the image recognition using CNN. The proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit( Re LU)layers,which combined multiple deeper network layers. After being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine classes. Comparing with the widely used Res Net 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.