Due to the large amount of airborne multispectral light detection and ranging (MS LiDAR) point cloud data, it is required to annotate it to complete supervised learning. However, the annotation cost of large-scale point clouds is high, which can easily lead to incomplete or inaccurate annotation, affecting the accuracy of point cloud classification. Therefore, this article proposes a new weakly supervised MS LiDAR point cloud classification method based on kernel point convolutional semantic query network. Firstly, using kernel convolutional semantic query network to detect weak targets in point clouds. On this basis, sparsify the point cloud data. Introduce weakly supervised learning methods to classify MS LiDAR point clouds. The experimental results have verified that the research method can accurately classify different types of point cloud data, and the time consumption can be controlled within 5ms. Compared with traditional methods, it has significant application advantages.
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