A novel neural network for super-resolution remote sensing image reconstruction
文献类型：期刊论文
| 作者 | Huo, Xing1,2; Tang, Ronglin3 ; Ma, Lingling4; Shao, Kun1; Yang, YongHua5
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| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2019 |
| 卷号 | 40期号:5-6页码:2375-2385 |
| ISSN号 | 0143-1161 |
| DOI | 10.1080/01431161.2018.1516319 |
| 通讯作者 | Ma, Lingling(llm_1981@hotmail.com) |
| 英文摘要 | An accurate super-resolution image (SR image) reconstruction of remote sensing images (RSI) for preserving quality during the process of super-resolution conversion is crucial for many scientific and operational applications. Recent studies on supervised and unsupervised machine learning methodologies of SR image reconstruction have demonstrated their great potential for higher reconstruction performance in obtaining accuracy and quality. In this paper, a novel neural network with barycentric weight function (BWFNN) is proposed as a non-linear mapping function selected from the features of reference images. The whole process includes an online reconstruction phase and an offline training phase. In these phases, an edge orientation-based pre-learned kernel is introduced to describe and reference prior information, and a simple interpolation-like structure is followed to avoid any conventional iterative computation and lead to fast reconstruction. The innovation of this work is the BWFNN, which uses a non-linear barycentric weight function (BWF) to reconstruct the image details. Compared with most of the conventional reconstruction approaches, the proposed algorithm performs better in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and the model exhibits significant efficiency in reconstructing the image details. |
| WOS关键词 | MODEL |
| 资助项目 | National Natural Science Foundation of China[61502136] ; National Natural Science Foundation of China[61572167] ; International S&T Cooperation Program of China[2014DFE10220] ; International S&T Cooperation Program of China[2015DFA11450] |
| WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000464043900047 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China ; International S&T Cooperation Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/48048]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ma, Lingling |
| 作者单位 | 1.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Sch Math, Hefei, Anhui, Peoples R China 2.Univ Sci & Technol China, Sch Engn Sci, Hefei, Anhui, Peoples R China 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 4.Chinese Acad Sci, Acad Opto S, Key Lab Quantitat Remote Sensing Informat Technol, Beijing 100094, Peoples R China 5.North Anhui Sci & Technol Innovat Ctr, Long Kang Farm, Huaiyuan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huo, Xing,Tang, Ronglin,Ma, Lingling,et al. A novel neural network for super-resolution remote sensing image reconstruction[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2019,40(5-6):2375-2385. |
| APA | Huo, Xing,Tang, Ronglin,Ma, Lingling,Shao, Kun,&Yang, YongHua.(2019).A novel neural network for super-resolution remote sensing image reconstruction.INTERNATIONAL JOURNAL OF REMOTE SENSING,40(5-6),2375-2385. |
| MLA | Huo, Xing,et al."A novel neural network for super-resolution remote sensing image reconstruction".INTERNATIONAL JOURNAL OF REMOTE SENSING 40.5-6(2019):2375-2385. |
入库方式： OAI收割
来源：地理科学与资源研究所
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