An Improved Pulse-Coupled Neural Network Model for Pansharpening

被引：12
|
作者：
Li, Xiaojun [1 ,2 ]
Yan, Haowen [1 ,2 ]
Xie, Weiying [3 ]
Kang, Lu [4 ]
Tian, Yi [5 ]
机构：
[1] Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China
[2] Natl Local Joint Engn Res Ctr Technol & Applicat, Lanzhou 730070, Peoples R China
[3] Xidian Univ, State Key Lab Integrated Serv Network, Xian 710071, Peoples R China
[4] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
[5] Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Prov Key Lab Robot & Intelligent Syst, Shenzhen 518172, Peoples R China
来源：
SENSORS | 2020年 / 20卷 / 10期
基金：
中国国家自然科学基金; 国家重点研发计划; 中国博士后科学基金;
关键词：
multispectral image; pansharpening; pulse-coupled neural network; high-resolution image; image fusion; IMAGE FUSION; RESOLUTION; CONTRAST; QUALITY; PCNN;
D O I：
10.3390/s20102764
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
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.
引用
收藏
页数：20
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