Integrating Object Boundary in Super-Resolution Land-Cover Mapping
文献类型：期刊论文
| 作者 | Chen, Yuehong1; Ge, Yong1,2; Jia, Yuanxin1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2017 |
| 卷号 | 10期号:1页码:219-230 |
| 关键词 | Land cover object boundary remotely sensed imagery super-resolution mapping (SRM) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2016.2533571 |
| 通讯作者 | Chen, Yuehong(chenyh@lreis.ac.cn) |
| 英文摘要 | This paper proposes a novel class allocation strategy in units of object (UOO) for soft-then-hard super-resolution mapping (STHSRM). STHSRM involves two processes: 1) subpixel sharpening and 2) class allocation. The UOO is implemented in the second process by integrating the object boundaries as an additional structural constraint. First, UOO obtains the object boundaries from remote-sensing images by image segmentation. The number of subpixels within an object is then calculated for each class to meet the coherence constraint of fractional images imposed by soft classification. Finally, a linear optimization model is built for each object to obtain the optimal hard class labels of subpixels. A synthetic image and two real remote-sensing images are used to evaluate the effectiveness of UOO. The results are compared visually and quantitatively with two existing class allocation methods: 1) the highest attribute values first (HAVF) and 2) units of class (UOC). The experimental results show that UOO performs better than these twomethods. UOO can better reduce the salt and pepper effect in resultant maps than both HAVF and UOC when dealing with real remote-sensing images. Moreover, UOO can better maintain the structure of land-cover patches, with smoother boundaries as compared with the two methods. |
| WOS关键词 | REMOTELY-SENSED IMAGERY ; HOPFIELD NEURAL-NETWORK ; SHIFTED HYPERSPECTRAL IMAGERY ; SUB-PIXEL SCALES ; CLASSIFICATION OUTPUT ; SPATIAL DEPENDENCE ; SENSING IMAGERY ; MIXED PIXELS ; MAP MODEL ; INFORMATION |
| 资助项目 | National Natural Science Foundation of China[41471296] ; Key Technologies Research and Development Program of China[2012BAH33B01] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000391719900020 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Key Technologies Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/65169]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Yuehong |
| 作者单位 | 1.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Yuehong,Ge, Yong,Jia, Yuanxin. Integrating Object Boundary in Super-Resolution Land-Cover Mapping[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(1):219-230. |
| APA | Chen, Yuehong,Ge, Yong,&Jia, Yuanxin.(2017).Integrating Object Boundary in Super-Resolution Land-Cover Mapping.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(1),219-230. |
| MLA | Chen, Yuehong,et al."Integrating Object Boundary in Super-Resolution Land-Cover Mapping".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.1(2017):219-230. |
入库方式： OAI收割
来源：地理科学与资源研究所
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