Hyperspectral Image Classification via Neighborhood Adaptive Graph Isomorphism Network

Graph convolutional network (GCN) has garnered significant attention in hyperspectral image (HSI) classification due to their ability to model non-Euclidean structured data. Compared with convolutional neural network (CNN), GCN can perform convolutions over irregular image regions and learn global dependencies among pixels in the whole image. Most existing GCN-based methods in the HSI community rely on average aggregation or weighted average aggregation strategies to aggregate neighboring node features. This process tends to obscure the differences between the nodes. However, for HSI classification tasks with obvious intra-class variability, average aggregation is a suboptimal choice. Moreover, the quality of the initial graph structure plays a crucial role in the model’s capacity to represent spectral relationships effectively. To mitigate these issues, we propose a neighborhood adaptive graph isomorphism network (NAGIN) for HSI classification to ensure that the diversified spectra representation of land-cover can be effectively captured. The neighborhood adaptive block (NAB) enhances spectral discriminability between land-cover classes via spectral reconstruction, enabling more precise removal of anomalous pixels in neighboring nodes. The graph isomorphism network (GIN) aggregates the features of neighboring nodes in an isomorphic manner to obtain multiple spectral expressions of the same type of land-cover, ensuring that the spectral features of different land-cover classes can be accurately distinguished. The Kolmogorov-Arnold network (KAN) leverages its ability to learn adaptive activation functions to better extract and refine the spectral features aggregated by GIN. Experimental results demonstrate that NAB can effectively improve the quality of the graph structure, the GIN aggregation method is competitive in HSI classification, and the proposed NAGIN outperforms the state-of-the-art methods on several public HSI datasets.