부산대학교 도서관

The mining of meaningful features and effective fusion of multisource remote sensing (RS) data have always been the challenging research problems in the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. In this article, we propose a multifrequency graph convolutional network with cross-modality mutual enhancement (MFGCN-CME) for multisource RS data classification. Specifically, we design an adaptive multifrequency graph feature learning module to capture the low- and high-frequency multiscale features of HSI and LiDAR in parallel and further adaptively aggregate them. Then, we propose a bipartite graph (BG) enhancement learning module to obtain the spatial-enhanced HSI features and spectral-enhanced LiDAR features by propagating intermodality information. To the best of our knowledge, the BG is first used to multisource RS data classification task. Furthermore, compared with traditional fusion methods, a gated fusion module is used to fully explore the complementarity of two data sources. Finally, a joint loss function combing a classification loss and a semisupervised contrastive loss is developed to improve the model robustness. Comprehensive experiments on different HSI and LiDAR datasets demonstrate that our proposed method can yield better performance compared with several state-of-the-art multisource RS data classification methods.