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Pansharpening, a pivotal task in remote sensing, involves integratinglow-resolution multispectral images with high-resolution panchromatic images tosynthesize an image that is both high-resolution and retains multispectralinformation. These pansharpened images enhance precision in land coverclassification, change detection, and environmental monitoring within remotesensing data analysis. While deep learning techniques have shown significantsuccess in pansharpening, existing methods often face limitations in theirevaluation, focusing on restricted satellite data sources, single scene types,and low-resolution images. This paper addresses this gap by introducingPanBench, a high-resolution multi-scene dataset containing all mainstreamsatellites and comprising 5,898 pairs of samples. Each pair includes afour-channel (RGB + near-infrared) multispectral image of 256x256 pixels and amono-channel panchromatic image of 1,024x1,024 pixels. To achieve high-fidelitysynthesis, we propose a Cascaded Multiscale Fusion Network (CMFNet) forPansharpening. Extensive experiments validate the effectiveness of CMFNet. Wehave released the dataset, source code, and pre-trained models in thesupplementary, fostering further research in remote sensing.

PanBench: Towards High-Resolution and High-Performance Pansharpening