IGSNRR OpenIR
Object-Based Area-to-Point Regression Kriging for Pansharpening
Zhang, Yihang1; Atkinson, Peter M.2; Ling, Feng1; Foody, Giles M.3; Wang, Qunming4; Ge, Yong5; Li, Xiaodong1; Du, Yun1
2021-10-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume59Issue:10Pages:8599-8614
Corresponding AuthorLing, Feng(lingf@whigg.ac.cn)
AbstractOptical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with a fine spatial resolution (FR) panchromatic (PAN) image. Pansharpening is a technique applied to such satellite sensor images to generate an FR MS image by injecting spatial detail taken from the FR PAN image while simultaneously preserving the spectral information of MS image. Pansharpening methods are mostly applied on a per-pixel basis and use the PAN image to extract spatial detail. However, many land cover objects in FR satellite sensor images are not illustrated as independent pixels, but as many spatially aggregated pixels that contain important semantic information. In this article, an object-based pansharpening approach, termed object-based area-to-point regression kriging (OATPRK), is proposed. OATPRK aims to fuse the MS and PAN images at the object-based scale and, thus, takes advantage of both the unified spectral information within the CR MS images and the spatial detail of the FR PAN image. OATPRK is composed of three stages: image segmentation, object-based regression, and residual downscaling. Three data sets acquired from IKONOS and Worldview-2 and 11 benchmark pansharpening algorithms were used to provide a comprehensive assessment of the proposed OATPRK approach. In both the synthetic and real experiments, OATPRK produced the most superior pan-sharpened results in terms of visual and quantitative assessment. OATPRK is a new conceptual method that advances the pixel-level geostatistical pansharpening approach to the object level and provides more accurate pan-sharpened MS images.
KeywordDownscaling geostatistics image fusion object-based pansharpening segmentation
DOI10.1109/TGRS.2020.3041724
WOS KeywordREMOTE-SENSING DATA ; IMAGE FUSION ; SPATIAL-RESOLUTION ; MODIS IMAGES ; LANDSAT-TM ; CLASSIFICATION ; SEGMENTATION ; INFORMATION ; MULTIRESOLUTION ; ALGORITHMS
Indexed BySCI
Language英语
Funding ProjectKey Research Program of Frontier Sciences, Chinese Academy of Sciences[ZDBS-LY-DQC034] ; National Natural Science Foundation of China[41801292] ; National Natural Science Foundation of China[41971297] ; Hubei Provincial Natural Science Foundation for Innovation Groups[2019CFA019] ; Natural Science Foundation of Hubei Province[2018CFB274] ; Hubei Province Natural Science Fund for Distinguished Young Scholars[2018CFA062]
Funding OrganizationKey Research Program of Frontier Sciences, Chinese Academy of Sciences ; National Natural Science Foundation of China ; Hubei Provincial Natural Science Foundation for Innovation Groups ; Natural Science Foundation of Hubei Province ; Hubei Province Natural Science Fund for Distinguished Young Scholars
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000698968700044
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/165910
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLing, Feng
Affiliation1.Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Key Lab Monitoring & Estimate Environm & Disaster, Wuhan 430077, Peoples R China
2.Univ Lancaster, Fac Sci & Technol, Lancaster Environm Ctr, Lancaster LA1 4YQ, England
3.Univ Nottingham, Sch Geog, Nottingham NG7 2RD, England
4.Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Yihang,Atkinson, Peter M.,Ling, Feng,et al. Object-Based Area-to-Point Regression Kriging for Pansharpening[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(10):8599-8614.
APA Zhang, Yihang.,Atkinson, Peter M..,Ling, Feng.,Foody, Giles M..,Wang, Qunming.,...&Du, Yun.(2021).Object-Based Area-to-Point Regression Kriging for Pansharpening.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(10),8599-8614.
MLA Zhang, Yihang,et al."Object-Based Area-to-Point Regression Kriging for Pansharpening".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.10(2021):8599-8614.
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