Correction: FeatureB2SENet: point cloud classification of large scenes

With the continuous development of 3D data acquisition technology in recent years, it is more and more convenient to obtain the point cloud data of large scenes, which contains a variety of rich information. How to effectively and accurately classify and segment point cloud data of large scenes has become a research hot-spot in the field of computer vision. In this paper, we study the method based on clustering, make full use of the spatial location and context information, and propose a new network framework, FeatureB2SENet. In the 2D and 3D projection feature calculation, we generate a 32× 32× 1 feature image for each point and input it into the convolution neural network to process the feature image. Finally, a comprehensive verification analysis is carried out on GML _ A, GML _ B and Vaihingen data sets, which proves that the classification effect is better.