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Linear spectral unmixing using endmember coexistence rules and spatial correlation
Ma, Tianxiao1,2,3; Li, Runkui1,4; Svenning, Jens-Christian3,5,6; Song, Xianfeng1,2,3,4
2018
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
Volume39Issue:11Pages:3512-3536
Corresponding AuthorLi, Runkui(lirk@ucas.ac.cn)
AbstractMixed 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.
DOI10.1080/01431161.2018.1444288
WOS KeywordMIXTURE ANALYSIS ; HYPERSPECTRAL IMAGERY ; END-MEMBERS ; EXTRACTION ; INFORMATION ; REGRESSION ; MODEL
Indexed BySCI
Language英语
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000428581700004
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57315
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLi, Runkui
Affiliation1.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
Recommended Citation
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.
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