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A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery
Meng, Fan1; Yang, Xiaomei1,2; Zhou, Chenghu1; Li, Zhi3,4
2017-09-01
Source PublicationSENSORS
ISSN1424-8220
Volume17Issue:9Pages:16
Corresponding AuthorYang, Xiaomei(yangxm@lreis.ac.cn)
AbstractCloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.
Keywordsparse representation dictionary learning image inpainting thick clouds removal high resolution remote sensing image
DOI10.3390/s17092130
WOS KeywordMULTITEMPORAL IMAGES ; SENSED IMAGES ; REPRESENTATION ; RECONSTRUCTION ; DECOMPOSITION ; RESTORATION ; ALGORITHM ; SIGNAL ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41601396] ; National Natural Science Foundation of China[41671436] ; National Key Research and Development Program of China[2016YFB0501404] ; LREIS[O88RAA01YA] ; China Postdoctoral Science Foundation[2015M580131]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China ; LREIS ; China Postdoctoral Science Foundation
WOS Research AreaChemistry ; Electrochemistry ; Instruments & Instrumentation
WOS SubjectChemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS IDWOS:000411484700200
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62077
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYang, Xiaomei
Affiliation1.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
3.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Meng, Fan,Yang, Xiaomei,Zhou, Chenghu,et al. A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery[J]. SENSORS,2017,17(9):16.
APA Meng, Fan,Yang, Xiaomei,Zhou, Chenghu,&Li, Zhi.(2017).A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.SENSORS,17(9),16.
MLA Meng, Fan,et al."A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery".SENSORS 17.9(2017):16.
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