Multiscale Spiking Graph Convolution Aggregation Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification remains challenging due to complex spectral–spatial patterns, the growing demand for energy-efficient modeling in resource-constrained remote sensing applications, and the structural limitations of existing graph convolutional network (GCN)- and spiking neural network (SNN)-based methods. Conventional GCNs rely on static graphs and coarse neighborhood aggregation, limiting their ability to model non-Euclidean spectral–spatial relations, while most SNN-bas