IGSNRR OpenIR
A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images
Wang, Zhihua1,2; Yang, Xiaomei1,3; Lu, Chen1,2; Yang, Fengshuo1,2
2018-07-01
Source PublicationINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN0303-2434
Volume69Pages:88-98
Corresponding AuthorYang, Xiaomei(yangxm@lreis.ac.cn)
AbstractAutomatic updating of land use/cover change (LUCC) databases using high spatial resolution images (HSRI) is important for environmental monitoring and policy making, especially for coastal areas that connect the land and coast and that tend to change frequently. Many object-based change detection methods are proposed, especially those combining historical LUCC with HSRI. However, the scale parameter(s) segmenting the serial temporal images, which directly determines the average object size, is hard to choose without experts' intervention. And the samples transferred from historical LUCC also need experts' intervention to avoid insufficient or wrong samples. With respect to the scale parameter(s) choosing, a Scale Self-Adapting Segmentation (SSAS) approach based on the exponential sampling of a scale parameter and location of the local maximum of a weighted local variance was proposed to determine the scale selection problem when segmenting images constrained by LUCC for detecting changes. With respect to the samples transferring, Knowledge Transfer (KT), a classifier trained on historical images with LUCC and applied in the classification of updated images, was also proposed. Comparison experiments were conducted in a coastal area of Zhujiang, China, using SPOT 5 images acquired in 2005 and 2010. The results reveal that (1) SSAS can segment images more effectively without intervention of experts. (2) KT can also reach the maximum accuracy of samples transfer without experts' intervention. Strategy SSAS + KT would be a good choice if the temporal historical image and LUCC match, and the historical image and updated image are obtained from the same resource.
KeywordOcean disaster Coastal area Land use/cover change High spatial resolution images GEOBIA Scale issue
DOI10.1016/j.jag.2018.03.001
WOS KeywordREMOTE-SENSING IMAGES ; ACCURACY ASSESSMENT MEASURES ; OBJECT-BASED CLASSIFICATION ; CHANGE-VECTOR ANALYSIS ; COVER CHANGE ; MULTISCALE SEGMENTATION ; PARAMETER SELECTION ; SATELLITE IMAGES ; DISCREPANCY MEASURE ; TIME-SERIES
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFC1402003] ; National Science Foundation of China[41671436] ; National Science Foundation of China[41421001] ; Innovation Project of LREIS[O88RAA01YA]
Funding OrganizationNational Key Research and Development Program of China ; National Science Foundation of China ; Innovation Project of LREIS
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000431156700008
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54981
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, 11A Datun Rd, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Wang, Zhihua,Yang, Xiaomei,Lu, Chen,et al. A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2018,69:88-98.
APA Wang, Zhihua,Yang, Xiaomei,Lu, Chen,&Yang, Fengshuo.(2018).A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,69,88-98.
MLA Wang, Zhihua,et al."A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 69(2018):88-98.
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