Spectral-Spatial Interpretation

Post-hoc image explanations usually reveal where a model looks through spatial heatmaps, or which channels matter through global channel scores. This separation is limiting for multi-spectral data, where channel relevance is often spatially localized and physically meaningful. We introduce Spectral-Spatial Interpretation (SSI), a full-grid attribution method over position-channel players (g, c). For each spatial position, SSI fixes the observed surrounding context, defines a conditional game over the channels at that position, and applies the Shapley value to this local game. The result is a full spectral-spatial attribution matrix rather than a single heatmap or a global channel ranking. SSI has a clear local semantics: it satisfies Shapley fairness within each position, and its channel aggregation exactly recovers the position-level contextual contrast obtained by replacing that entire position with the baseline. We also distinguish SSI from context-averaged Owen-style hierarchical accounting, which respects the same position-channel grouping but averages over external spatial coalitions. On HLS Burn Scar, SSI outperforms an MC-Owen estimator across retained attribution metrics while using only about 6% of its evaluation budget. Across CUB-200, EuroSAT, and HLS Burn Scar under both per-channel mean and zero baselines, SSI achieves the best joint deletion and insertion AUC over dense, flat, and rank-1 baselines, yields the strongest spectral hit rates and channel deletion, and leads spatial localization on both annotated datasets.