Objective  Full-waveform airborne LiDAR can not only obtain 3D point clouds, but also obtain full-waveform of the target. The classification of full-waveform LiDAR data is to assign a category label to each point based on data characteristics. It is an important part of data post-processing and has significant application value in the fields such as remote sensing and topographic mapping. However, existing methods overlook the correlation between full-waveform and point clouds geometry in terms of physical characteristics, and lack in-depth exploration of the correlation in neighborhood geometry and semantic features between point pairs. Therefore, the existing methods lack the ability to capture local structural information, which affects the classification effect. To address the above issues, a classification method based on the physical and geometric characteristics of the target is proposed. This method mines the high-dimensional feature association between 3D point clouds and full-waveforms on the high-dimensional physical characteristics of the target, and enhances the learning of local geometric structural information, thereby improving the classification ability of the model.

  Methods  A classification method based on target physical and geometric features is proposed. Firstly, a high-dimensional feature fusion module is proposed, which extracted the rich physical characteristics from the full-waveform, and extracted the geometric features of the point clouds. Based on the correlation between full-waveforms and point clouds on the physical characteristics of the target, the complex relationship between the two is learned through a dual low-rank matrix, and deeper physical features are excavated (Fig.2). Secondly, a local neighborhood feature enhancement module is designed to enhance the learning of local geometric structures by constructing a fully-connected neighborhood structure, mining the geometric and semantic correlations between neighboring point pairs (Fig.3). Finally, by using the hierarchical encoder-decoder structure, the characteristics of multiple receptive field can be conbined. A classification method based on the physical characteristics and geometric characteristics can be constructed to improve the classification ability of the model (Fig.4).

  Results and Discussions   The proposed method is based on the correlation between point clouds and full-waveforms on the physical characteristics of the target. By fusing the two, the physical meaning of the features is enriched and the learning of local geometric structures is enhanced. From Tab.2, it can be seen that the proposed method successfully generated correct labels for most points, achieving an average accuracy of 0.96, a recall of 0.90, and an F1 score of 0.92. Multiple methods such as FCN, GACNN, and FWNet2 are used for testing on the same dataset. Compared with FWNet2, which has the best performance among existing methods, our method has effective improvements in average accuracy, average recall, and average F1 score. Among them, compared with FWNet2, the accuracy, recall, and F1 score of our method have been improved by 0.02, 0.01, and 0.02 respectively in the ground category, and by 0.02, 0.04, and 0.03 respectively in the street category. The test results are shown (Fig.6).

  Conclusions  This article proposes a classification algorithm based on the physical and geometric characteristics of the target. This method focuses on the rich physical characteristics and vertical structural information of the target contained in the full-waveform, as well as the correlation between full-waveforms and geometric features of point clouds. The fusion of the two features is achieved using a dual low-rank matrix; And based on the local neighborhood fully-connected structure, the learning of local structural information are strengthened, thus constructing an end-to-end full-waveform airborne LiDAR data classification network. Multiple experiments show that the average accuracy and recall of our classification method are as high as 0.96 and 0.90, respectively, indicating the effectiveness of this method. At the same time, it provides some possibilities for exploration in-depth feature extraction and fusion, based on the correlation in target physical characteristics between full-waveforms and point clouds.