Multiscale adaptive reconstruction of missing information for remotely sensed data using sparse representation

doi:10.1080/2150704X.2018.1439198

	Multiscale adaptive reconstruction of missing information for remotely sensed data using sparse representation
	Meng, Fan 1; Yang, Xiaomei 1,2; Zhou, Chenghu 1; Li, Zhi 3,4; Liu, Bin 5
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.