SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion

doi:10.1016/j.rse.2019.111537

	SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion
	Li, Xiaodong 3; Foody, Giles M.2; Boyd, Doreen S.2; Ge, Yong 1; Zhang, Yihang 3; Du, Yun 3; Ling, Feng 3
刊名	REMOTE SENSING OF ENVIRONMENT
	2020-02-01
卷号	237 页码:15
关键词	Spatio-temporal image fusion Land cover class fraction FSDAF
ISSN号	0034-4257
DOI	10.1016/j.rse.2019.111537
通讯作者	Li, Xiaodong(lixiaodong@whigg.ac.cn)
英文摘要	Spatio-temporal image fusion methods have become a popular means to produce remotely sensed data sets that have both fine spatial and temporal resolution. Accurate prediction of reflectance change is difficult, especially when the change is caused by both phenological change and land cover class changes. Although several spatio-temporal fusion methods such as the Flexible Spatiotemporal DAta Fusion (FSDAF) directly derive land cover phenological change information (such as endmember change) at different dates, the direct derivation of land cover class change information is challenging. In this paper, an enhanced FSDAF that incorporates sub-pixel class fraction change information (SFSDAF) is proposed. By directly deriving the sub-pixel land cover class fraction change information the proposed method allows accurate prediction even for heterogeneous regions that undergo a land cover class change. In particular, SFSDAF directly derives fine spatial resolution endmember change and class fraction change at the date of the observed image pair and the date of prediction, which can help identify image reflectance change resulting from different sources. SFSDAF predicts a fine resolution image at the time of acquisition of coarse resolution images using only one prior coarse and fine resolution image pair, and accommodates variations in reflectance due to both natural fluctuations in class spectral response (e.g. due to phenology) and land cover class change. The method is illustrated using degraded and real images and compared against three established spatio-temporal methods. The results show that the SFSDAF produced the least blurred images and the most accurate predictions of fine resolution reflectance values, especially for regions of heterogeneous landscape and regions that undergo some land cover class change. Consequently, the SFSDAF has considerable potential in monitoring Earth surface dynamics.
资助项目	Hubei Province Natural Science Fund for Distinguished Young Scholars[2018CFA062] ; Youth Innovation Promotion Association CAS[2017384] ; Natural Science Foundation of China[61671425] ; Hubei Provincial Natural Science Foundation for Innovation Groups[2019CFA019] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA 2003030201] ; National Science Fund for Distinguished Young Scholars[41725006]
WOS关键词	REFLECTANCE FUSION ; TIME-SERIES ; LANDSAT ; MODIS ; ALGORITHM ; MODEL ; CONSEQUENCES ; DYNAMICS ; FEATURES ; NETWORK
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000509819300016
资助机构	Hubei Province Natural Science Fund for Distinguished Young Scholars ; Youth Innovation Promotion Association CAS ; Natural Science Foundation of China ; Hubei Provincial Natural Science Foundation for Innovation Groups ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Science Fund for Distinguished Young Scholars
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/132336]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Xiaodong
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Nottingham, Sch Geog, Nottingham NG7 2RD, England 3.Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Environm & Disaster Monitoring & Evaluat, Wuhan 430077, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaodong,Foody, Giles M.,Boyd, Doreen S.,et al. SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion[J]. REMOTE SENSING OF ENVIRONMENT,2020,237:15.
APA	Li, Xiaodong.,Foody, Giles M..,Boyd, Doreen S..,Ge, Yong.,Zhang, Yihang.,...&Ling, Feng.(2020).SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion.REMOTE SENSING OF ENVIRONMENT,237,15.
MLA	Li, Xiaodong,et al."SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion".REMOTE SENSING OF ENVIRONMENT 237(2020):15.