FMGRPA: Flow Matching with Group-Relative Preference Alignment for Pansharpening

Pansharpening aims to fuse a low resolution multispectral (LRMS) image and a high resolution panchromatic (PAN) image to recover a high resolution multispectral (HRMS) image with spectral fidelity and spatial detail. Existing methods usually reconstruct paired HRMS images, but a single objective constrains only overall reference error, making it difficult to distinguish spectral shifts from insufficient spatial detail or express the desired spectral and spatial fusion preference among results with comparable reconstruction errors. To this end, we propose FMGRPA, a two stage pansharpening framework combining LRMS anchored conditional flow matching with group relative preference alignment. Stage I takes upsampled LRMS as the flow source and learns a PAN guided conditional vector field toward the HRMS reference, yielding a base prediction initialized from observed multispectral information. Stage II constructs a task specific candidate group around this prediction for spectral enhancement, spatial enhancement, and balanced fusion, then jointly evaluates the candidates by spectral relation consistency, spatial relation consistency, and reference fidelity. Softmax normalization within each group converts candidate rewards into relative supervision weights that guide refinement toward a better spectral and spatial balance. Furthermore, spatial preference aggregation combines global and local evidence, while temporal preference aggregation integrates relative evaluations across flow time conditions to improve preference stability. Extensive experiments demonstrate competitive performance and validate explicit group relative fusion preferences for generative pansharpening.