Topology-aware dynamic high-order graph learning for hyperspectral image classification

• Topology-aware dynamic high-order graph learning proves robustness & high performance. • Dynamic high-order graph module enables boundary & spatial topology perception. • Spectral-spatial image convolution module reduces redundant band & mine local feature. • Spectral-spatial feature fusion module captures rich spectral-spatial features. Hyperspectral image (HSI) classification is challenging due to intra-class spectral variability caused by mixed pixels at land cover boundaries in complex spatial distributions. Additionally, the classification model easily suffers from overfitting due to high-dimensional features and limited available samples. Recently, GCNs have emerged to handle these challenges, as they enable convolution on non-Euclidean data with arbitrary structures and modeling topological relationships between nodes. However, existing GCNs either use predefined adjacency matrices that cannot be updated throughout the training process or perform feature aggregation only between the central node and its first-order neighbors, which hinders the building of spatial topology. This paper introduces a novel topology-aware dynamic high-order graph learning (TDHGL) to address the aforementioned issues. Specifically, we first introduce a spectral-spatial image convolution module (SICM) to reduce spectral redundancy and extract local spectral-spatial features. Then, we design a dynamic high-order graph module (DHGM) to extract the spatial topology of the HSI. It allows for dynamic updating of adjacency matrices and facilitates high-order message interactions between the central node and its higher-order neighbors, mitigating classification inaccuracies induced by intra-class spectral variability while enhancing the TDHGL’s generalization capacity. Finally, the spectral-spatial feature fusion module (SFFM) aggregates the local spectral-spatial features with spatial topology to capture rich spectral-spatial features. Our TDHGL achieves overall accuracies of 94.37 %, 97.17 %, 95.87 %, and 98.21 % on the Indian Pines, Salinas, University of Pavia, and WHU-Hi-LongKou datasets, respectively, and outperforms other state-of-the-art methods.