KGLP-Net: Kalman-Reliable Global-Local Learning With Group Fisher Pruning for Hyperspectral Image Classification

Existing global-local HSI classifiers rarely account for the relative uncertainty of complementary information sources, while model compression typically ignores the uncertainty estimated during fusion. To address this limitation, we propose **Kalman-Reliable Global–Local Learning with Group Fisher Pruning (KGLP-Net)**, which couples uncertainty-aware fusion with reliability-guided structured pruning. KGLP-Net employs an asymmetric dual-view architecture, where a Global Mamba Encoder captures long-range contextual dependencies and a Local CNN Encoder preserves fine-grained target-neighborhood information, providing complementary representations with distinct uncertainty characteristics. **Kalman-Reliable Fusion (KRF)** treats the global and local representations as a prior and an observation, respectively, and performs an uncertainty-guided, Kalman-inspired posterior update to jointly estimate a fused posterior representation and its uncertainty. **Kalman-Reliable Group Fisher Pruning (KRGF)** further transfers the posterior uncertainty into group-level reliability via normalized posterior precision and integrates it with Group Fisher importance, allowing the uncertainty to directly inform structured pruning. On all four public HSI datasets, Compact48, the KRGF-compressed model with hidden width 48, achieves the highest OA among ten representative competing classifiers. Compared with Dense64, it reduces parameters, FLOPs, and inference time by 38.5%, 34.8%, and 37.6% on average, respectively, while incurring only a 0.03 percentage point decrease in mean OA.