2014/05/10 by Alexis Huck, Huck, Alexis, François de Vieilleville +5
Computer Science · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Remote-Sensing Image Classification #cs.CV
paper · pdf · doi:10.48550/arxiv.1405.2403
4 pages, detailed version of proceedings of conference IEEE WHISPERS 2014
arxiv created 2014/05/10 · openalex publication_date 2014/05/10 · arxiv updated 2014/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we address the issue of hyperspectral pan-sharpening, which consists in fusing a (low spatial resolution) hyperspectral image HX and a (high spatial resolution) panchromatic image P to obtain a high spatial resolution hyperspectral image. The problem is addressed under a variational convex constrained formulation. The objective favors high resolution spectral bands with level lines parallel to those of the panchromatic image. This term is balanced with a total variation term as regularizer. Fit-to-P data and fit-to-HX data constraints are effectively considered as mathematical constraints, which depend on the statistics of the data noise measurements. The developed Alternating Direction Method of Multipliers (ADMM) optimization scheme enables us to solve this problem efficiently despite the non differentiabilities and the huge number of unknowns.