@inproceedings{fa37040e105042d6bba4f93fe3e7dfc6,
title = "A Multi-Level Supervised Network for Pansharpening to Reduce Color Distortion",
abstract = "Due to the inherent limitations of satellites, obtaining high-resolution multispectral (MS) images directly poses a challenge. Consequently, several pansharpening methods have been proposed to fuse panchromatic (Pan) images with MS images in order to generate high-resolution MS images. However, the resulting fused images often suffer from color distortion. To address this issue, we developed a multi-level supervised network aimed at minimizing color distortion. Our approach disassembled the pansharpening method into two models: an image generation module and a color optimization module. The image generation module was responsible for producing an initial fused image with rich texture, while the color optimization module focused on correcting the grey distribution of each band to achieve a high-fidelity fused image. Through experiments conducted on GaoFen-2, we have demonstrated significant improvements in reducing color distortion using our proposed method.",
keywords = "color distortion, image fusion, multi-level supervised network, remote sensing",
author = "Jian Guo and Ziyang Kong and Qizhi Xu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 ; Conference date: 16-07-2023 Through 21-07-2023",
year = "2023",
doi = "10.1109/IGARSS52108.2023.10282258",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6811--6814",
booktitle = "IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}