气候变化领域集成服务门户(CCPortal): Sub-pixel mapping with point constraints

CCPortal

DOI	10.1016/j.rse.2020.111817
	Sub-pixel mapping with point constraints
	Wang Q.; Zhang C.; Atkinson P.M.
发表日期	2020
ISSN	00344257
卷号	244
英文摘要	Remote sensing images contain abundant land cover information. Due to the complex nature of land cover, however, mixed pixels exist widely in remote sensing images. Sub-pixel mapping (SPM) is a technique for predicting the spatial distribution of land cover classes within mixed pixels. As an ill-posed inverse problem, the uncertainty of prediction cannot be eliminated and hinders the production of accurate sub-pixel maps. In contrast to conventional methods that use continuous geospatial information (e.g., images) to enhance SPM, in this paper, a SPM method with point constraints into SPM is proposed. The method of fusing point constraints is implemented based on the pixel swapping algorithm (PSA) and utilizes the auxiliary point information to reduce the uncertainty in the SPM process and increase map accuracy. The point data are incorporated into both the initialization and optimization processes of PSA. Experiments were performed on three images to validate the proposed method. The influences of the performances were also investigated under different numbers of point data, different spatial characters of land cover and different zoom factors. The results show that by using the point data, the proposed SPM method can separate more small-sized targets from aggregated artifacts and the accuracies are increased obviously. The proposed method is also more accurate than the advanced radial basis function interpolation-based method. The advantage of using point data is more evident when the point data size and scale factor are large and the spatial autocorrelation of the land cover is small. As the amount of point data increases, however, the increase in accuracy becomes less noticeable. Furthermore, the SPM accuracy can still be increased even if the point data and coarse proportions contain errors. © 2020 Elsevier Inc.
英文关键词	Downscaling; Pixel swapping algorithm (PSA); Point constraints; Remote sensing images; Sub-pixel mapping (SPM); Super-resolution mapping
语种	英语
scopus关键词	Aggregates; Image enhancement; Inverse problems; Mapping; Radial basis function networks; Remote sensing; Spatial variables measurement; Conventional methods; Geo-spatial informations; ILL-posed inverse problem; Land cover informations; Radial basis function interpolation; Remote sensing images; Spatial autocorrelations; Sub-pixel mapping; Pixels; algorithm; autocorrelation; land cover; mapping method; pixel; remote sensing; satellite imagery; spatial distribution
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/179323
作者单位	College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai, 200092, China; Faculty of Science and Technology, Lancaster University, Lancaster, LA1 4YR, United Kingdom; Geography and Environment, University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom; Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing, 100101, China
推荐引用方式 GB/T 7714	Wang Q.,Zhang C.,Atkinson P.M.. Sub-pixel mapping with point constraints[J],2020,244.
APA	Wang Q.,Zhang C.,&Atkinson P.M..(2020).Sub-pixel mapping with point constraints.Remote Sensing of Environment,244.
MLA	Wang Q.,et al."Sub-pixel mapping with point constraints".Remote Sensing of Environment 244(2020).