Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening

Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening

Dear Editor, In pansharpening task, the most existing deep-learning-based pan-sharpening methods fail to fully utilize the different level features, inevitably leading to spectral or spatial distortions. To address this challenge, in this letter, we propose a dual-branch multi-level feature aggregat...

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Veröffentlicht in:IEEE/CAA journal of automatica sinica 2022-11, Vol.9 (11), p.2023-2026
Hauptverfasser: Cheng, Gui, Shao, Zhenfeng, Wang, Jiaming, Huang, Xiao, Dang, Chaoya
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Graphical user interface
Source code
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container_end_page 2026
container_issue 11
container_start_page 2023
container_title IEEE/CAA journal of automatica sinica
container_volume 9
creator Cheng, Gui
Shao, Zhenfeng
Wang, Jiaming
Huang, Xiao
Dang, Chaoya
description Dear Editor, In pansharpening task, the most existing deep-learning-based pan-sharpening methods fail to fully utilize the different level features, inevitably leading to spectral or spatial distortions. To address this challenge, in this letter, we propose a dual-branch multi-level feature aggregation network for pansharpening (DMFANet). The experimental results on the WorldView-II (WV-II) and QuickBird (QB) dataset confirmed the notable superiority of our method over the current state-of-the-art methods from quantitative and qualitative point of view. The source code is available at https://github.com/Gui-Cheng/DMFANet.
doi_str_mv 10.1109/JAS.2022.105956
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subjects Agglomeration
Graphical user interface
Source code
title Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening
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