气候变化领域集成服务门户(CCPortal): Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach

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DOI	10.1016/j.jag.2018.07.014
	Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach
	Khiali L.; Ndiath M.; Alleaume S.; Ienco D.; Ose K.; Teisseire M.
发表日期	2019
ISSN	15698432
起始页码	103
结束页码	119
卷号	74
英文摘要	The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas. © 2018 Elsevier B.V.
英文关键词	Clustering; Graph analysis; Inter-site analysis; Object-oriented image analysis; Satellite image time series
语种	英语
scopus关键词	annual variation; cluster analysis; detection method; image analysis; remote sensing; satellite imagery; spatiotemporal analysis; time series
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156567
作者单位	TETIS, APT, CIRAD, CNRS, IRSTEA, UNIV. Montpellier, Montpellier, France; LIRMM, Montpellier, France
推荐引用方式 GB/T 7714	Khiali L.,Ndiath M.,Alleaume S.,et al. Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach[J],2019,74.
APA	Khiali L.,Ndiath M.,Alleaume S.,Ienco D.,Ose K.,&Teisseire M..(2019).Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach.International Journal of Applied Earth Observation and Geoinformation,74.
MLA	Khiali L.,et al."Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach".International Journal of Applied Earth Observation and Geoinformation 74(2019).