Graph-Feature-Enhanced Selective Assignment Network for Hyperspectral and Multispectral Data Classification

Due to rich spectral and spatial information, the combination of hyperspectral and multispectral images (MSIs) has been widely used for Earth observation, such as wetland classification. However, mining of meaningful features and effective fusion of multisource remote sensing data are still urgent problems to be solved. In this article, graph-feature-enhanced selective assignment network (GSANet) is proposed. On the one hand, a graph feature extraction module (GFEM) is designed to extract topological structure information and combine with the rich spectral-spatial information. In particular, the features obtained by convolution are first mapped to the graph feature space, and the graph convolution operation is used to achieve propagation between nodes for preserving topological structure information. Moreover, to reduce the difference of graph features resulting from the mapping function and better explore the complementary properties of multisource data, a novel graph fusion strategy-graph dependence fusion is designed. A transition graph is generated to enhance the association and interaction between different graph features, so as to avoid the information loss caused by simple fusion operation. On the other hand, a selective feature assignment module (SFAM) is developed to adaptively assign weights to different discriminative features. SFAM assigns weights to different features to selectively emphasize informative features and suppress less useful ones. Extensive experiments are conducted on two multisource remote sensing datasets, and the improvement of at least 1.27% and 0.98% compared to other state-of-the-art work demonstrates the superiority of the proposed GSANet.