典型文献
Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening
文献摘要：
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 experimen-tal 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 .
文献关键词：
中图分类号：
作者姓名：
Gui Cheng;Zhenfeng Shao;Jiaming Wang;Xiao Huang;Chaoya Dang
作者机构：
State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;Department of Geosciences,University of Arkansas,Fayetteville,AR 72701 USA
文献出处：
引用格式：
[1]Gui Cheng;Zhenfeng Shao;Jiaming Wang;Xiao Huang;Chaoya Dang-.Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening)[J].自动化学报（英文版）,2022(11):2023-2026
A类：
Pansharpening,DMFANet
B类：
Dual,Branch,Multi,Level,Feature,Aggregation,Network,Dear,Editor,
In,pansharpening,task,most,existing,deep,learning,methods,fail,fully,utilize,different,level,features,inevitably,leading,spectral,spatial,distortions,To,address,this,challenge,letter,we,propose,dual,branch,multi,aggregation,network,experimen,tal,results,WorldView,WV,II,QuickBird,QB,dataset,confirmed,notable,superiority,over,current,state,art,from,quantitative,qualitative,point,view,source,code,available
AB值：
0.722372
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