气候变化领域集成服务门户(CCPortal): SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion

CCPortal

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 X.; Foody G.M.; Boyd D.S.; Ge Y.; Zhang Y.; Du Y.; Ling F.
发表日期	2020
ISSN	00344257
卷号	237
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. © 2019 Elsevier Inc.
英文关键词	FSDAF; Land cover class fraction; Spatio-temporal image fusion
语种	英语
scopus关键词	Forecasting; Image enhancement; Pixels; Reflection; Woolen and worsted fabrics; Fine-resolution images; FSDAF; Heterogeneous landscapes; Land cover; Spatial and temporal resolutions; Spatio-temporal fusions; Spatio-temporal methods; Spatiotemporal images; Image fusion; accuracy assessment; image analysis; image resolution; land cover; phenology; pixel; prediction; remote sensing; satellite data; spatiotemporal analysis; surface reflectance
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179546
作者单位	Key Laboratory for Environment and Disaster Monitoring and Evaluation, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, Hubei 430077, China; School of Geography, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
推荐引用方式 GB/T 7714	Li X.,Foody G.M.,Boyd D.S.,et al. SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion[J],2020,237.
APA	Li X..,Foody G.M..,Boyd D.S..,Ge Y..,Zhang Y..,...&Ling F..(2020).SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion.Remote Sensing of Environment,237.
MLA	Li X.,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).