When Pansharpening Meets Graph Convolution Network and Knowledge Distillation

doi:10.1109/TGRS.2022.3168192

	When Pansharpening Meets Graph Convolution Network and Knowledge Distillation
	Yan, Keyu 1,2; Zhou, Man 1,2; Liu, Liu 3; Xie, Chengjun 4; Hong, Danfeng 5
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2022
卷号	60
关键词	Pansharpening Convolution Satellites Feature extraction Task analysis Knowledge engineering Spatial resolution Asynchronous knowledge distillation atrous convolution graph convolutional network (GCN) pansharpening
ISSN号	0196-2892
DOI	10.1109/TGRS.2022.3168192
通讯作者	Xie, Chengjun(cjxie@iim.ac.cn)
英文摘要	In this article, we propose a novel graph convolutional network (GCN) for pansharpening, defined as GCPNet, which consists of three main modules: the spatial GCN module (SGCN), the spectral band GCN module (BGCN), and the atrous spatial pyramid module (ASPM). Specifically, due to the nature of GCN, the proposed SGCN and BGCN are capable of exploring the long-range relationship between the object and the global state in the spatial and spectral aspects, which benefits pansharpened results and has not been fully investigated before. In addition, the designed ASPM is equipped with multiscale atrous convolutions and learns richer local feature information, so as to cover the objects of different sizes in satellite images. To further enhance the representation of our proposed GCPNet, asynchronous knowledge distillation is introduced to provide compact features by heterogeneous task imitation in a teacher-student paradigm. In the paradigm, the teacher network acts as a variational autoencoder to extract compact features of the ground-truth MS images. The student network, devised for pansharpening, is trained with the assistance of the teacher network to transfer the important information of the expected ground-truth MS images. Extensive experimental results on different satellite datasets demonstrate that our proposed network outperforms the state-of-the-art methods both visually and quantitatively. The source code is released at https://github.com/Keyu-Yan/GCPNet.
资助项目	National Natural Science Foundation of China[32171888]
WOS关键词	NEURAL-NETWORK ; FUSION ; IMAGES
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000788976800001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/131553]
专题	中国科学院合肥物质科学研究院
通讯作者	Xie, Chengjun
作者单位	1.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Sci Isl Branch, Hefei 230026, Peoples R China 3.Shanghai Jiao Tong Univ, Dept Comp Sci, Shanghai 200240, Peoples R China 4.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 5.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China
推荐引用方式 GB/T 7714	Yan, Keyu,Zhou, Man,Liu, Liu,et al. When Pansharpening Meets Graph Convolution Network and Knowledge Distillation[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60.
APA	Yan, Keyu,Zhou, Man,Liu, Liu,Xie, Chengjun,&Hong, Danfeng.(2022).When Pansharpening Meets Graph Convolution Network and Knowledge Distillation.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60.
MLA	Yan, Keyu,et al."When Pansharpening Meets Graph Convolution Network and Knowledge Distillation".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022).