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    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.03.003

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Enhancing spectral unmixing by considering the point spread function effect

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Enhancing spectral unmixing by considering the point spread function effect. / Wang, Qunming; Shi, Wenzhong; Atkinson, Peter M.
In: Spatial Statistics, Vol. 28, 12.2018, p. 271-283.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Wang Q, Shi W, Atkinson PM. Enhancing spectral unmixing by considering the point spread function effect. Spatial Statistics. 2018 Dec;28:271-283. Epub 2018 Mar 18. doi: 10.1016/j.spasta.2018.03.003

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Wang, Qunming ; Shi, Wenzhong ; Atkinson, Peter M. / Enhancing spectral unmixing by considering the point spread function effect. In: Spatial Statistics. 2018 ; Vol. 28. pp. 271-283.

Bibtex

@article{54b390da7c824865a8750077e9a51b89,
title = "Enhancing spectral unmixing by considering the point spread function effect",
abstract = "The 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 unmxing. 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 unmxing 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 sub-pixel 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.",
keywords = "Land cover, Spectral unmixing, Soft classification, Point spread function (PSF), Area-to-point-kriging (ATPK)",
author = "Qunming Wang and Wenzhong Shi and Atkinson, {Peter M.}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.03.003",
year = "2018",
month = dec,
doi = "10.1016/j.spasta.2018.03.003",
language = "English",
volume = "28",
pages = "271--283",
journal = "Spatial Statistics",
issn = "2211-6753",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Enhancing spectral unmixing by considering the point spread function effect

AU - Wang, Qunming

AU - Shi, Wenzhong

AU - Atkinson, Peter M.

N1 - This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.03.003

PY - 2018/12

Y1 - 2018/12

N2 - The 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 unmxing. 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 unmxing 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 sub-pixel 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.

AB - The 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 unmxing. 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 unmxing 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 sub-pixel 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.

KW - Land cover

KW - Spectral unmixing

KW - Soft classification

KW - Point spread function (PSF)

KW - Area-to-point-kriging (ATPK)

U2 - 10.1016/j.spasta.2018.03.003

DO - 10.1016/j.spasta.2018.03.003

M3 - Journal article

VL - 28

SP - 271

EP - 283

JO - Spatial Statistics

JF - Spatial Statistics

SN - 2211-6753

ER -