Enhancing spectral unmixing by considering the point spread function effect
Wang, Qunming1,2,3; Shi, Wenzhong4; Atkinson, Peter M.2,5,6,7
Corresponding AuthorWang, Qunming(wqm11111@126.com)
AbstractThe point spread function (PSF) effect exists ubiquitously in real remotely sensed data and such that the observed pixel signal is not only determined by the land cover within its own spatial coverage but also by that within neighboring pixels. The PSF, thus, imposes a fundamental limit on the amount of information captured in remotely sensed images and it introduces great uncertainty in the widely applied, inverse goal of spectral unmixing. Until now, spectral unmixing has erroneously been performed by assuming that the pixel signal is affected only by the land cover within the pixel, that is, ignoring the PSF. In this paper, a new method is proposed to account for the PSF effect within spectral unmixing to produce more accurate predictions of land cover proportions. Based on the mechanism of the PSF effect, the mathematical relation between the coarse proportion and sub-pixel proportions in a local window was deduced. Area-to-point kriging (ATPK) was then proposed to find a solution for the inverse prediction problem of estimating the sub-pixel proportions from the original coarse proportions. The subpixel proportions were finally upscaled using an ideal square wave response to produce the enhanced proportions. The effectiveness of the proposed method was demonstrated using two datasets. The proposed method has great potential for wide application since spectral unmixing is an extremely common approach in remote sensing. (C) 2018 Elsevier B.V. All rights reserved.
KeywordLand cover Spectral unmixing Soft classification Point spread function (PSF) Area-to-point-kriging (ATPK)
Indexed BySCI
Funding ProjectOne Thousand Youth Talent Program[0250141304] ; Research Grants Council of Hong Kong[PolyU 15223015] ; National Natural Science Foundation of China[41331175]
Funding OrganizationOne Thousand Youth Talent Program ; Research Grants Council of Hong Kong ; National Natural Science Foundation of China
WOS Research AreaGeology ; Mathematics ; Remote Sensing
WOS SubjectGeosciences, Multidisciplinary ; Mathematics, Interdisciplinary Applications ; Remote Sensing ; Statistics & Probability
WOS IDWOS:000451116200018
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Document Type期刊论文
Corresponding AuthorWang, Qunming
Affiliation1.Tongji Univ, Coll Surveying & Geoinformat, 1239 Siping Rd, Shanghai 200092, Peoples R China
2.Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England
3.Ctr Ecol & Hydrol, Lancaster LA1 4YQ, England
4.Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China
5.Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England
6.Queens Univ Belfast, Sch Geog Archaeol & Palaeoecol, Belfast BT7 1NN, Antrim, North Ireland
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Wang, Qunming,Shi, Wenzhong,Atkinson, Peter M.. Enhancing spectral unmixing by considering the point spread function effect[J]. SPATIAL STATISTICS,2018,28:271-283.
APA Wang, Qunming,Shi, Wenzhong,&Atkinson, Peter M..(2018).Enhancing spectral unmixing by considering the point spread function effect.SPATIAL STATISTICS,28,271-283.
MLA Wang, Qunming,et al."Enhancing spectral unmixing by considering the point spread function effect".SPATIAL STATISTICS 28(2018):271-283.
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