An Improved Pulse-Coupled Neural Network Model for Pansharpening

doi:10.3390/s20102764

	An Improved Pulse-Coupled Neural Network Model for Pansharpening
	Li, Xiaojun 3,4; Yan, Haowen 3,4; Xie, Weiying 1; Kang, Lu 5; Tian, Yi 2
刊名	SENSORS
	2020-05-01
卷号	20 期号:10 页码:20
关键词	multispectral image pansharpening pulse-coupled neural network high-resolution image image fusion
DOI	10.3390/s20102764
通讯作者	Yan, Haowen(yanhw@lzjtu.edu.cn)
英文摘要	Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.
资助项目	National Key R&D Program of China[2017YFB0504201] ; National Natural Science Foundation of China[41861055] ; National Natural Science Foundation of China[41671447] ; National Natural Science Foundation of China[41761082] ; China Postdoctoral Science Foundation[2019M653795] ; lzjtu EP Program[201806]
WOS关键词	IMAGE FUSION ; RESOLUTION ; CONTRAST ; QUALITY ; PCNN
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	MDPI
WOS记录号	WOS:000539323700018
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; lzjtu EP Program
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/162186]
专题	中国科学院地理科学与资源研究所
通讯作者	Yan, Haowen
作者单位	1.Xidian Univ, State Key Lab Integrated Serv Network, Xian 710071, Peoples R China 2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Prov Key Lab Robot & Intelligent Syst, Shenzhen 518172, Peoples R China 3.Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China 4.Natl Local Joint Engn Res Ctr Technol & Applicat, Lanzhou 730070, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaojun,Yan, Haowen,Xie, Weiying,et al. An Improved Pulse-Coupled Neural Network Model for Pansharpening[J]. SENSORS,2020,20(10):20.
APA	Li, Xiaojun,Yan, Haowen,Xie, Weiying,Kang, Lu,&Tian, Yi.(2020).An Improved Pulse-Coupled Neural Network Model for Pansharpening.SENSORS,20(10),20.
MLA	Li, Xiaojun,et al."An Improved Pulse-Coupled Neural Network Model for Pansharpening".SENSORS 20.10(2020):20.