Linear spectral unmixing using endmember coexistence rules and spatial correlation

doi:10.1080/01431161.2018.1444288

	Linear spectral unmixing using endmember coexistence rules and spatial correlation
	Ma, Tianxiao 1,2,3; Li, Runkui 1,4; Svenning, Jens-Christian 3,5,6; Song, Xianfeng 1,2,3,4
刊名	INTERNATIONAL JOURNAL OF REMOTE SENSING
	2018
卷号	39 期号:11 页码:3512-3536
ISSN号	0143-1161
DOI	10.1080/01431161.2018.1444288
通讯作者	Li, Runkui(lirk@ucas.ac.cn)
英文摘要	Mixed pixels are often formed when surface materials are smaller than the spatial resolution of a sensor, or two or more ground features fall within a pixel. Spectral unmixing, decomposing a mixed pixel into a set of endmembers and their corresponding abundance fractions, is an important method for extracting the underlying spectral and spatial information from remote sensing images. Recent studies have shown that it is difficult to increase the accuracy of unmixing using single pixel processing. Here, we suggest combining information on the fundamental interrelations of ground components and a priori knowledge on how ground components co-exist or exclude each other according to general geographic and geomorphic relations with spectral information may allow improved unmixing. Therefore, we propose a novel spectral unmixing method to estimate endmember abundances based on linear spectral mixing model with endmember coexistence rules and spatial correlation (LSMM-R&C). This method was implemented by incorporating endmember coexistence rules along with spatial correlation into a weighted least square method. Experiments with both synthetic and real satellite images were carried out to verify the proposed method, and its performance was also evaluated in comparison to the commonly used LSMM (linear spectral mixture method), LAU (local adaptive unmixing), ISU (iterative spectral unmixing) and ISMA (iterative spectral mixture analysis) methods. LSMM-R&C showed the smallest error, and was more effective at revealing the detailed spatial distribution of endmembers' abundance, showing high potential for solving the problem of spatial heterogeneity among neighbouring pixels.
WOS关键词	MIXTURE ANALYSIS ; HYPERSPECTRAL IMAGERY ; END-MEMBERS ; EXTRACTION ; INFORMATION ; REGRESSION ; MODEL
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000428581700004
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/57314]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Runkui
作者单位	1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Univ Chinese Acad Sci, Sino Danish Coll, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sino Danish Educ & Res Ctr, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 5.Aarhus Univ, Sect Ecoinformat & Biodivers, Dept Biosci, Aarhus C, Denmark 6.Aarhus Univ, Dept Biosci, Ctr Biodivers Dynam Changing World BIOCHANGE, Aarhus C, Denmark
推荐引用方式 GB/T 7714	Ma, Tianxiao,Li, Runkui,Svenning, Jens-Christian,et al. Linear spectral unmixing using endmember coexistence rules and spatial correlation[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2018,39(11):3512-3536.
APA	Ma, Tianxiao,Li, Runkui,Svenning, Jens-Christian,&Song, Xianfeng.(2018).Linear spectral unmixing using endmember coexistence rules and spatial correlation.INTERNATIONAL JOURNAL OF REMOTE SENSING,39(11),3512-3536.
MLA	Ma, Tianxiao,et al."Linear spectral unmixing using endmember coexistence rules and spatial correlation".INTERNATIONAL JOURNAL OF REMOTE SENSING 39.11(2018):3512-3536.