Dual-Space Knowledge Distillation with Cross-Geometric Feature Interaction for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is critical for remote sensing but faces challenges in balancing accuracy and inference efficiency. Existing graph-based knowledge distillation (KD) methods are confined to single geometric spaces, ignoring the hierarchical semantics of land-cover categories. Cross-geometry distillation compresses multiple geometries into a single Euclidean student, forcing one geometry to collapse into the other. In this paper, we propose Dual-Space Knowledge Distillation (DSKD), a novel dual-student dual-space KD framework integrating Euclidean (GCN) and Hyperbolic (HGCN) teachers to jointly train a native MLP student and a native HNN student. With cross-geometric feature bridging (CGFB) and output distribution cohesion (ODC), the two students mutually learn each other’s complementary geometry, so DSKD captures complementary spatial-spectral and hierarchical features while enabling graph-free inference without message passing. Extensive experiments on four HSI datasets and four general graph benchmarks demonstrate that DSKD outperforms single-space distillation and single-student cross-geometry baselines in most settings, confirming its effectiveness and generalization capability across diverse graph-structured data.