LAUSR

Airborne laser scanning (ALS) point cloud segmentation is an essential procedure for 3D data understanding and applications. This task is challenging due to the unstructured, disordered, and sparse distribution of the point cloud. PointNet++ is a well known end-to-end learning network for point cloud segmentation without fully exploring the local and contextual features, which are less efficient and accurate in capturing the complexity of point clouds. On this basis, we design a novel encoder-decoder network architecture to obtain the semantic features of the ALS point cloud at different levels and achieve a better segmentation effect. The improved local feature aggregation module can merge the deep feature of the point cloud, combining local and global self-attention convolutional networks. It can adaptively explore the inherent semantics feature of points and capture more extensive context information of ALS point cloud, termed DSPNet++. Finally, the conditional random field optimization model can be used to refine the segmentation results. We evaluated the performance of our method on the Vaihingen dataset of the International Society for Photogrammetry and Remote Sensing (ISPRS) and the GML(B) 3D dataset. Experimental results show that our method fully exploits the semantic feature of the ALS point cloud and can achieve higher accuracy. A comparative study with established deep learning models also confirms that our proposed method has outstanding performance in the ALS point cloud segmentation task.