OneHSI: A Unified Hyperspectral Foundation Model with Physical Consistency

Hyperspectral imaging (HSI) promises material-level perception beyond RGB vision, yet current models remain tightly coupled to sensing hardware. Minor changes in spectral discretization or spatial resolution can invalidate trained networks, despite all sensors observing the same material reflectance physics. We introduce OneHSI, a unified hyperspectral foundation model that aligns representations across heterogeneous sensing configurations through physically motivated inductive biases. Rather than treating bands and resolutions as arbitrary tensor dimensions, OneHSI reparameterizes spectral channels in a shared continuous wavelength space, separates scale-invariant relational attention from scale-conditioned feature transformation, and balances cross-domain contributions to learn sensor-invariant representations. Cross-domain experiments demonstrate state-of-the-art performance and strong robustness under sensor and scale shifts, supporting physics-aligned representation learning as a principled path toward cross-sensor generalization.