Multiscale adaptive reconstruction of missing information for remotely sensed data using sparse representation | |
Meng, Fan1; Yang, Xiaomei1,2; Zhou, Chenghu1; Li, Zhi3,4; Liu, Bin5 | |
刊名 | REMOTE SENSING LETTERS |
2018 | |
卷号 | 9期号:5页码:457-466 |
ISSN号 | 2150-704X |
DOI | 10.1080/2150704X.2018.1439198 |
通讯作者 | Meng, Fan(mengf@lreis.ac.cn) ; Yang, Xiaomei(yangxm@lreis.ac.cn) |
英文摘要 | Due to the influence of sensor malfunction and poor atmospheric condition, missing information is inevitable in optical remotely sensed (RS) data, which limits the availability of RS data. To tackle the inverse problem of missing information recovery, a multiscale adaptive patch reconstruction method was proposed in this letter. Multiscale dictionaries were learned from different sizes of exemplars in the known image region, which were later utilized to infer missing information patch-by-patch via sparse representation. Structure sparsity was incorporated to encourage the filling-in of missing patch on image structures and determine the patch size for further inpainting. Neighboring information was employed to restrain the appearance of the estimated patch, to yield semantically consistent inpainting result. In view of these ideas, we formulate the optimization model of adaptive patch inpainting and reconstruct missing information through a multiscale scheme. Experiments are performed on cloud removal, gaps filling and quantitative product reconstruction, which demonstrate that our method can well preserve spatially continuous structures and consistent textures without artifacts. |
资助项目 | National Key Research and Development Program of China[2016YFB0501404] ; National Natural Science Foundation of China[41601396] ; National Natural Science Foundation of China[41671436] ; China Postdoctoral Science Foundation[2015M580131] |
WOS关键词 | CLOUD REMOVAL ; IMAGE |
WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
语种 | 英语 |
出版者 | TAYLOR & FRANCIS LTD |
WOS记录号 | WOS:000427171300001 |
资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation |
内容类型 | 期刊论文 |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/57212] |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Meng, Fan; Yang, Xiaomei |
作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China 3.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Fujian Normal Univ, Coll Geog Sci, Fuzhou, Fujian, Peoples R China |
推荐引用方式 GB/T 7714 | Meng, Fan,Yang, Xiaomei,Zhou, Chenghu,et al. Multiscale adaptive reconstruction of missing information for remotely sensed data using sparse representation[J]. REMOTE SENSING LETTERS,2018,9(5):457-466. |
APA | Meng, Fan,Yang, Xiaomei,Zhou, Chenghu,Li, Zhi,&Liu, Bin.(2018).Multiscale adaptive reconstruction of missing information for remotely sensed data using sparse representation.REMOTE SENSING LETTERS,9(5),457-466. |
MLA | Meng, Fan,et al."Multiscale adaptive reconstruction of missing information for remotely sensed data using sparse representation".REMOTE SENSING LETTERS 9.5(2018):457-466. |
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