SEPARATE AND VARY TOGETHER: CROSS-SENSOR PANSHARPENING WITH REVERSIBLE SPECTRAL TRANSPORT AND PAIRED SENSOR TRANSFORMATIONS

Pansharpening fuses a low-resolution multispectral (MS) image with a panchromatic (PAN) image. Existing cross-sensor methods rely on per-sensor training, multi-sensor data, or target-time adaptation, which are costly and require target labels or weight updates. We study a restrictive setting: train once on a source sensor and deploy the same weights on unseen sensors. The challenge is that different sensors observe different band counts, centers, and spectral responses, and the PAN–MS relation varies. A fixed input–output mapping learned on one sensor is therefore tied to that sensor’s observation process and difficult to transfer. We instead learn how the output should change when the sensing process changes, formulated as approximate equivariance under sensor transformations: fusion should commute with a sampled transformation applied jointly to the source MS observations and their HRMS targets. Since the transformation is sampled from source data and paired targets are exact, this behavior is supervised without target data, enabling source-only training. We propose EquiPan, which promotes this approximate equivariance through two complementary mechanisms. A Spectral–Contrast Transformation (SCT) first samples virtual observation models from source data and applies each transformation consistently to the MS observations and their HRMS target, providing exact supervision of how the fusion output should change with the sensor. A Reversible Spectral Transport (RST) then projects the resulting heterogeneous observations into a shared latent space and maps predicted residuals back to their native bands through an analytic left inverse on the observed spectral degrees of freedom. A parameter-free PAN mean alignment (PMA) operation removes branch-dependent global offsets while preserving the measured PAN contrast. Across seven backbones and three unseen sensors, EquiPan improves zero-shot transfer over traditional and interpolation-only baselines.