IGSNRR OpenIR
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
2018
Source PublicationREMOTE SENSING LETTERS
ISSN2150-704X
Volume9Issue:5Pages:457-466
Corresponding AuthorMeng, Fan(mengf@lreis.ac.cn) ; Yang, Xiaomei(yangxm@lreis.ac.cn)
AbstractDue 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.
DOI10.1080/2150704X.2018.1439198
WOS KeywordCLOUD REMOVAL ; IMAGE
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000427171300001
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57213
Collection中国科学院地理科学与资源研究所
Corresponding AuthorMeng, Fan; Yang, Xiaomei
Affiliation1.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
Recommended Citation
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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