NAOC Open IR
Object-Based Wetland Classification Using Multi-Feature Combination of Ultra-High Spatial Resolution Multispectral Images
Geng, Renfang1; Jin, Shuanggen1,2; Fu, Bolin3; Wang, Bin4
2021-01-06
Source PublicationCANADIAN JOURNAL OF REMOTE SENSING
ISSN0703-8992
Pages19
AbstractThe Unmanned Aerial Vehicle (UAV) and Google Earth (GE) RGB images have ultra-high spatial resolution. But it is difficult to get a high classification accuracy due to the poor spectral resolution. In this article, the object-based wetland classification is investigated using multi-feature combination of ultra-high spatial resolution multispectral images (MSI). A Gram-Schmidt (GS) transformation is used to fuze Sentinel-2A data with UAV and GE RGB images, respectively, in order to obtain the ultra-high spatial resolution MSI as data sources. Three different feature combination classification scenarios are constructed for fusion GE and UAV MSI, respectively, based on selected features. The object-based random forest (RF) algorithms with parameters (mtry and ntree) optimization are used to carry out finer wetland classification. Results show that the fusion GE and UAV MSI have good applicability in the finer wetland classification, especially the fusion UAV images, and integrating multi-source features could improve classification accuracy. Both data sources reach the highest accuracy in scenario3. The overall accuracy of fusion UAV image scenario3 is 94.31% (Kappa = 0.9353), and that of fusion GE image scenario3 is 87.37% (Kappa = 0.8528). The contribution of different features to wetland classification is obtained with spectral and vegetation indexes, texture, geometric and contextual features.
Funding Organizationstrategic priority research program project of the Chinese Academy of Sciences ; strategic priority research program project of the Chinese Academy of Sciences ; Jiangsu Province Distinguished Professor Project ; Jiangsu Province Distinguished Professor Project ; Startup Foundation for Introducing Talent of NUIST ; Startup Foundation for Introducing Talent of NUIST ; strategic priority research program project of the Chinese Academy of Sciences ; strategic priority research program project of the Chinese Academy of Sciences ; Jiangsu Province Distinguished Professor Project ; Jiangsu Province Distinguished Professor Project ; Startup Foundation for Introducing Talent of NUIST ; Startup Foundation for Introducing Talent of NUIST ; strategic priority research program project of the Chinese Academy of Sciences ; strategic priority research program project of the Chinese Academy of Sciences ; Jiangsu Province Distinguished Professor Project ; Jiangsu Province Distinguished Professor Project ; Startup Foundation for Introducing Talent of NUIST ; Startup Foundation for Introducing Talent of NUIST ; strategic priority research program project of the Chinese Academy of Sciences ; strategic priority research program project of the Chinese Academy of Sciences ; Jiangsu Province Distinguished Professor Project ; Jiangsu Province Distinguished Professor Project ; Startup Foundation for Introducing Talent of NUIST ; Startup Foundation for Introducing Talent of NUIST
DOI10.1080/07038992.2021.1872374
Language英语
Funding Projectstrategic priority research program project of the Chinese Academy of Sciences[XDA23040100] ; Jiangsu Province Distinguished Professor Project[R2018T20] ; Startup Foundation for Introducing Talent of NUIST
Funding Organizationstrategic priority research program project of the Chinese Academy of Sciences ; strategic priority research program project of the Chinese Academy of Sciences ; Jiangsu Province Distinguished Professor Project ; Jiangsu Province Distinguished Professor Project ; Startup Foundation for Introducing Talent of NUIST ; Startup Foundation for Introducing Talent of NUIST ; strategic priority research program project of the Chinese Academy of Sciences ; strategic priority research program project of the Chinese Academy of Sciences ; Jiangsu Province Distinguished Professor Project ; Jiangsu Province Distinguished Professor Project ; Startup Foundation for Introducing Talent of NUIST ; Startup Foundation for Introducing Talent of NUIST ; strategic priority research program project of the Chinese Academy of Sciences ; strategic priority research program project of the Chinese Academy of Sciences ; Jiangsu Province Distinguished Professor Project ; Jiangsu Province Distinguished Professor Project ; Startup Foundation for Introducing Talent of NUIST ; Startup Foundation for Introducing Talent of NUIST ; strategic priority research program project of the Chinese Academy of Sciences ; strategic priority research program project of the Chinese Academy of Sciences ; Jiangsu Province Distinguished Professor Project ; Jiangsu Province Distinguished Professor Project ; Startup Foundation for Introducing Talent of NUIST ; Startup Foundation for Introducing Talent of NUIST
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000608906700001
PublisherTAYLOR & FRANCIS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/79948
Collection中国科学院国家天文台
Corresponding AuthorJin, Shuanggen; Fu, Bolin
Affiliation1.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
2.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
3.Guilin Univ Technol, Coll Geomat & Geoinformat, Guilin 541004, Peoples R China
4.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China
Recommended Citation
GB/T 7714
Geng, Renfang,Jin, Shuanggen,Fu, Bolin,et al. Object-Based Wetland Classification Using Multi-Feature Combination of Ultra-High Spatial Resolution Multispectral Images[J]. CANADIAN JOURNAL OF REMOTE SENSING,2021:19.
APA Geng, Renfang,Jin, Shuanggen,Fu, Bolin,&Wang, Bin.(2021).Object-Based Wetland Classification Using Multi-Feature Combination of Ultra-High Spatial Resolution Multispectral Images.CANADIAN JOURNAL OF REMOTE SENSING,19.
MLA Geng, Renfang,et al."Object-Based Wetland Classification Using Multi-Feature Combination of Ultra-High Spatial Resolution Multispectral Images".CANADIAN JOURNAL OF REMOTE SENSING (2021):19.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Geng, Renfang]'s Articles
[Jin, Shuanggen]'s Articles
[Fu, Bolin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Geng, Renfang]'s Articles
[Jin, Shuanggen]'s Articles
[Fu, Bolin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Geng, Renfang]'s Articles
[Jin, Shuanggen]'s Articles
[Fu, Bolin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.