SS-RAM: Spatial–Spectral Attribution Interpretability for Hyperspectral Imagery

This work proposes a novel unified explainability framework “Spatial–Spectral Relevance Activation Mapping” (SS-RAM), designed for Hyperspectral Images (HSI) super-resolution models. SS-RAM integrates three attribution mechanisms: relevance conservation via layer-wise relevance propagation, spatial localization through higher-order gradient-based activation mapping, and spectral fidelity via band-wise contribution analysis. These attributions are adaptively fused using a band-specific learnable coefficient, optimized through a multi-objective loss that jointly enforces fidelity, conservation, and faithfulness without manual tuning of weighting parameters. This framework is implemented as a post-hoc analysis on a Generative Adversarial Network (GAN)-based HSI super-resolution backbone, and SS-RAM produces interpretable heatmaps that simultaneously capture where the model attends spatially and which spectral bands drive the reconstruction. The experiments on four benchmarked HSI datasets (Indian Pines, Salinas, Pavia University, and WHU-Hi-LongKou) demonstrate near-perfect relevance conservation (≈0.999), high faithfulness (≈0.99), and balanced attribution sparsity, consistently outperforming single-source explainability methods such as LRP, Grad-CAM++, and Shapley values. The proposed framework provides robust and physically meaningful insights into deep HSI generative models there by enhancing trust and interpretability in remote sensing applications.