A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening
文献类型：期刊论文
| 作者 | Xu, Xinyu1; Li, Xiaojun1,2,3; Li, Yikun1,2,3; Kang, Lu4; Ge, Junfei1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 15期号:17页码:22 |
| 关键词 | hyperspectral sharpening pulse-coupled neural network multispectral image remote sensing image fusion high-resolution image |
| DOI | 10.3390/rs15174205 |
| 通讯作者 | Li, Xiaojun(xjli@mail.lzjtu.cn) |
| 英文摘要 | Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method. |
| WOS关键词 | MULTISPECTRAL IMAGES ; FUSION ; FACTORIZATION ; ALGORITHM |
| 资助项目 | The authors are grateful to the editor and anonymous reviewers for their helpful and valuable suggestions. We also want to express our sincere gratitude to Jie Li for her support and help. |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001060654200001 |
| 出版者 | MDPI |
| 资助机构 | The authors are grateful to the editor and anonymous reviewers for their helpful and valuable suggestions. We also want to express our sincere gratitude to Jie Li for her support and help. |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/196862]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Xiaojun |
| 作者单位 | 1.Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China 2.Natl Local Joint Engn Res Ctr Technol & Applicat N, Lanzhou 730070, Peoples R China 3.Gansu Prov Engn Lab Natl Geog State Monitoring, Lanzhou 730070, 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 |
| 推荐引用方式 GB/T 7714 | Xu, Xinyu,Li, Xiaojun,Li, Yikun,et al. A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening[J]. REMOTE SENSING,2023,15(17):22. |
| APA | Xu, Xinyu,Li, Xiaojun,Li, Yikun,Kang, Lu,&Ge, Junfei.(2023).A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening.REMOTE SENSING,15(17),22. |
| MLA | Xu, Xinyu,et al."A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening".REMOTE SENSING 15.17(2023):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。