气候变化领域集成服务门户(CCPortal): Spatio-temporal spectral unmixing of time-series images

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DOI	10.1016/j.rse.2021.112407
	Spatio-temporal spectral unmixing of time-series images
	Wang Q.; Ding X.; Tong X.; Atkinson P.M.
发表日期	2021
ISSN	00344257
卷号	259
英文摘要	Mixed pixels exist widely in remotely sensed images. To obtain more reliable land cover information than traditional hard classification, spectral unmixing methods have been developed to estimate the composition of the mixed pixels, in terms of the proportions of land cover classes. The existing spectral unmixing methods usually require pure spectra (i.e., endmembers) of each land cover class. However, in areas dominated by mixed pixels (e.g., highly heterogeneous areas), it can be a great challenge to extract a large number of pure endmembers, especially for long time-series data. Meanwhile, intra-class spectral variation remains a long-standing issue in spectral unmixing. In this paper, we propose a spatio-temporal spectral unmixing (STSU) approach to address these issues. The proposed method extends spectral unmixing from the traditional spatial domain to the spatio-temporal domain. It exploits fully the multi-scale spatio-temporal information, by using temporally neighboring fine spatial resolution images to detect land cover changes and, further, extracts the proportion information of unchanged mixed pixels required for training. The STSU method is free of the need for endmember extraction, using directly the extracted mixed training samples to construct a learning model, and it accounts for intra-class spectral variation. Therefore, it is a fully automatic method suitable for dynamic monitoring of land cover changes. The effectiveness of the STSU method was validated through experiments on Moderate Resolution Imaging Spectroradiometer (MODIS) data in five different areas. The proposed STSU method provides a new solution for spectral unmixing of time-series data based on the goal of continuous monitoring at the global scale. © 2021 The Author(s)
英文关键词	Change detection; Spatio-temporal domain; Spectral unmixing; Support vector machines (SVM); Time-series
语种	英语
scopus关键词	Classification (of information); Data mining; Extraction; Pixels; Radiometers; Support vector machines; Change detection; Endmembers; Land cover; Mixed pixel; Spatio-temporal; Spatio-temporal domains; Spectral unmixing; Support vector machine; Time-series data; Times series; Time series; detection method; image analysis; land cover; MODIS; pixel; remote sensing; satellite data; spatial resolution; time series analysis
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/178866
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
推荐引用方式 GB/T 7714	Wang Q.,Ding X.,Tong X.,et al. Spatio-temporal spectral unmixing of time-series images[J],2021,259.
APA	Wang Q.,Ding X.,Tong X.,&Atkinson P.M..(2021).Spatio-temporal spectral unmixing of time-series images.Remote Sensing of Environment,259.
MLA	Wang Q.,et al."Spatio-temporal spectral unmixing of time-series images".Remote Sensing of Environment 259(2021).