An Improved Pulse-Coupled Neural Network Model for Pansharpening
文献类型：期刊论文
| 作者 | Li, Xiaojun1,2; Yan, Haowen1,2; Xie, Weiying3; Kang, Lu4; Tian, Yi5 |
| 刊名 | SENSORS
 |
| 出版日期 | 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. |
| WOS关键词 | IMAGE FUSION ; RESOLUTION ; CONTRAST ; QUALITY ; PCNN |
| 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研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000539323700018 |
| 出版者 | MDPI |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; lzjtu EP Program |
| 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.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 |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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