Low-Intervention Boundary-Risk Graph Calibration for Cross-Domain Few-Shot Hyperspectral Image Classification

Hyperspectral sensors provide high-dimensional spectral-spatial observations for land-cover analysis, but reliable classification remains difficult when only a few target-scene labels are available. Cross-domain few-shot hyperspectral image classification usually depends on sparse support samples, so final decisions can be unstable in low-margin regions where repairable errors and correctly classified long-tail samples are entangled. We propose Boundary-Risk Graph Calibration (BRGC), a risk-controlled calibration framework that improves support-set decision reliability. BRGC combines boundary-aware mixability training with inference-time graph residual calibration. During inference, support labels are clamped, an unlabeled target-query graph provides structural smoothing evidence, and the original classification scores are modified only through low-margin gating and bounded residual updates. On 10 target datasets with 10 random seeds, BRGC consistently improves its base classifier and achieves the highest macro-average OA, AA, and Kappa among matched-protocol transductive baselines. Repair/damage diagnostics, ablation studies, parameter sensitivity analysis, and cross-method adaptation show that BRGC improves scarce-label hyperspectral image interpretation by converting query-graph structure into low-intervention reliability evidence.