The Road Network Extraction Based On Multi-Spectral LIDAR Point Cloud

Airborne laser radar(LiDAR)technology can obtain the high precision three-dimensional point cloud data of the earth's wide-range surface quickly,which acquisition system integrated with GPS and IMU,laser scanner and other equipment.While its active acquisition mode of the data,it has a certain penetration ability to the vegetation and the others,therefore it has the absolute advantage to get the ground objects three-dimension information quickly.Among all the target objects,the road is the most basic facilities in infrastructure of national economy,it is also one of the most important facilities.The rapid,accurate and timely acquisition and update of road information is of great significance to the realization and construction of "smart city"and "digital traffic".However,the existing road extraction method based on LiDAR data has the problem of the strong difference of reflection because of different materials,the difficulty in distinguishing the park,square from the road region,which usually leads to the phenomenon of partial misrepresentation of urban roads.Therefore,this paper comprehensively utilizes the reflection intensity information and coordinate information of the multi-spectral airborne LiDAR point cloud to obtain the road network quickly and accurately,and carries out the following research work:(1)Existing airborne LiDAR data filtering method are introduced,and the existing method of filtering is used to process the multispectral data.Based on the data after filtering,the multispectral data are fused.Summarization of several multi-spectral data fusion method are introduced later.(2)The statistics-based features of the fused multi-spectral point cloud data are calculated and understood in a simple way,which functions are verified by theory or experiments.(3)For the road the strip shape information,a local binary feature is put forward and designed which is applied to the road strip shape information.The points are divided into road points and non-road points through the random forest classifier which is trained by the features introduced above.(4)In the basis of the road points extracted,the mathematical morphology method of image processing is used to extract the road central line,and the final road network is obtained by vectorize the road central line.(5)As the road axis is obtained,the attributions of every points,such as the curvature,slope and the direction and so on.Due to the road points are also confirmed by above steps,we combined them with the road axis to extract the road boundary lines using the active contour model.It shows that:By obtaining the LiDAR point cloud's three-dimensional coordinates,the intensity of multispectral and strip information features,using the random forests classifier to extract the road can get a good result of point cloud,and it can also eliminate the large area of error points such as a large number of squares.On this basis,the central line of road point cloud is obtained by means of mathematical morphology to obtain a more complete road network,at the same time,some attributions are calculated and the road boundary is obtained as well.