Three-dimensional point clouds learning is widely applied, but the point clouds are still unable to deal with classification and recognition tasks satisfactorily in the cases of irregular geometric structures and high-dimensional space. In 3D space, point clouds tend to have regular Euclidean structure because of their density. On the contrary, due to the high dimensionality, the spatial structure of high-dimensional space is more complex, and point clouds are mostly presented in non-European structure. Furthermore, among current 3D point clouds classification algorithms, Canonical Capsules algorithm based on Euclidean distance is difficult to decompose and identify non-Euclidean structures effectively. Thus, aiming at the point clouds classification task of non-Euclidean structure in 3D and high-dimensional space, this paper refers to the LLE algorithm based on geodesic distance for optimizing and proposes the unsupervised algorithm of high-dimensional point clouds capsule. In this paper, the geometric features of point clouds are considered in the extraction process, so as to transform the high-dimensional non-Euclidean structure into a lower-dimensional Euclidean structure with retaining spatial geometric features. To verify the feasibility of the unsupervised algorithm of high-dimensional point clouds capsule, experiments are conducted in Swiss Roll dataset, point clouds MNIST dataset and point clouds LFW dataset. The results show that (1) non-Euclidean structures can be can effectively identified by this model in Swiss Roll dataset; (2) a significant unsupervised learning effect is realized in point clouds MNIST dataset. In conclusion, the high-dimensional point clouds capsule unsupervised algorithm proposed in this paper is conducive to expand the application scenarios of current point clouds classification and recognition tasks.

本文介绍了一种基于语义理解的多分支高维规范胶囊算法，通过对来自两种不同源数据的HSI和LiDAR数据的特征进行动态融合，实现了光谱-空间-高度融合特征的无监督提取，实验结果表明，该模型相较于现有模型具有更好的无监督提取多源遥感数据的有效性。

基于无监督多支路胶囊的高光谱和激光雷达分类