Unsupervised Domain Adaptation for Cross-Scene Multispectral Point Cloud Classification

Remote sensing cross-scene classification has always been an important research field, especially in the field of 3-D classification, which is of great significance. Considering the diversity of collection conditions, seasons, and regional styles, deep learning networks well-trained on one source domain dataset tend to suffer from severe performance degradation when applied to other target domain datasets. To tackle the issue, in this article, we propose a new cross-scene classification method, which combines pre-alignment and Shannon entropy constraint to accomplish unsupervised domain adaptive classification (PS-UDA). On the one hand, the pre-alignment employs $L_{2}$ -paradigm constraint and Laplace matrix to pre-align the features. With the $L_{2}$ -paradigm constraint, the originally distant features of the source and target domain are constrained to the same sphere surface, and it is easier to make the distribution alignment on the sphere surface. Further, the Laplace matrix is used to map the source and target domain. In this way, similar features of the source and target domain are further aligned, and dissimilar features become discrete from each other. On the other hand, this article employs the Shannon entropy constraint to motivate the network to obtain more high-confidence target domain pseudo-labels. In addition, to fully utilize the unlabeled target domain information, the target domain features are augmented using the adjacency matrix. Experimental results of two cross-scene multispectral point cloud classifications demonstrate that the proposed PS-UDA can effectively mitigate the spectral shift issue in cross-scene multispectral point clouds, achieving state-of-the-art performance.