Response-Pair Spherical Attention for Hyperspectral and Multispectral Image Fusion

Hyperspectral and multispectral image fusion (HMF) combines spatially coarse hyperspectral observations with fine-scale multispectral guidance to reconstruct a high-resolution hyperspectral image. In attention-based fusion, reconstruction quality depends on how neighboring responses are weighted relative to each target. Yet conventional dot-product attention couples candidate and target response amplitudes solely through their product. Consequently, response pairs with the same amplitude product and directional agreement receive identical affinity scores even when their relative amplitude allocations differ. We introduce response-pair spherical attention (RPSA), which decomposes each learned candidate–target response pair into overall effective amplitude, relative amplitude allocation, and orientation difference. Its affinity preserves sensitivity to joint response strength while using amplitude balance and directional agreement to regulate candidate contributions. We embed RPSA in RPSA-Net, an alternating cross-resolution architecture that alternates high-resolution feature refinement under multispectral guidance with feedback that updates a low-resolution hyperspectral state. Experiments on three simulated benchmarks and a real-world dataset demonstrate that RPSA-Net achieves state-of-the-art spectral fidelity and spatial reconstruction while maintaining fast inference.