PanBench: Towards High-Resolution and High-Performance Pansharpening
This work addresses a gap in remote sensing data analysis by providing a comprehensive dataset and method to enhance pansharpening for applications like land cover classification and environmental monitoring, though it is incremental in nature.
The paper tackles the problem of limited evaluation in pansharpening by introducing PanBench, a high-resolution multi-scene dataset with 5,898 sample pairs from mainstream satellites, and proposes CMFNet, which achieves high-fidelity synthesis validated through extensive experiments.
Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information. These pansharpened images enhance precision in land cover classification, change detection, and environmental monitoring within remote sensing data analysis. While deep learning techniques have shown significant success in pansharpening, existing methods often face limitations in their evaluation, focusing on restricted satellite data sources, single scene types, and low-resolution images. This paper addresses this gap by introducing PanBench, a high-resolution multi-scene dataset containing all mainstream satellites and comprising 5,898 pairs of samples. Each pair includes a four-channel (RGB + near-infrared) multispectral image of 256x256 pixels and a mono-channel panchromatic image of 1,024x1,024 pixels. To achieve high-fidelity synthesis, we propose a Cascaded Multiscale Fusion Network (CMFNet) for Pansharpening. Extensive experiments validate the effectiveness of CMFNet. We have released the dataset, source code, and pre-trained models in the supplementary, fostering further research in remote sensing.