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    Rights statement: This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. 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 Remote Sensing of Environment, 251, 2020 DOI: 10.1016/j.rse.2020.112054

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General solution to reduce the point spread function effect in subpixel mapping

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General solution to reduce the point spread function effect in subpixel mapping. / Wang, Q.; Zhang, C.; Tong, X.; Atkinson, P.M.

In: Remote Sensing of Environment, Vol. 251, 112054, 15.12.2020.

Research output: Contribution to journalJournal articlepeer-review

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Wang, Q. ; Zhang, C. ; Tong, X. ; Atkinson, P.M. / General solution to reduce the point spread function effect in subpixel mapping. In: Remote Sensing of Environment. 2020 ; Vol. 251.

Bibtex

@article{2e689bcd00ff4be09a8535a6cd08d1c5,
title = "General solution to reduce the point spread function effect in subpixel mapping",
abstract = "The point spread function (PSF) effect is ubiquitous in remote sensing images and imposes a fundamental uncertainty on subpixel mapping (SPM). The crucial PSF effect has been neglected in existing SPM methods. This paper proposes a general model to reduce the PSF effect in SPM. The model is applicable to any SPM methods treating spectral unmixing as pre-processing. To demonstrate the advantages of the new technique it was necessary to develop a new approach for accuracy assessment of SPM. To-date, accuracy assessment for SPM has been limited to subpixel classification accuracy, ignoring the performance of reproducing spatial structure in downscaling. In this paper, a new accuracy index is proposed which considers SPM performances in classification and restoration of spatial structure simultaneously. Experimental results show that by considering the PSF effect, more accurate SPM results were produced and small-sized patches and elongated features were restored more satisfactorily. Moreover, using the novel accuracy index, the quantitative evaluation was found to be more consistent with visual evaluation. This paper, thus, addresses directly two of the longest standing challenges in SPM (i.e., the limitations of the PSF effect and accuracy assessment undertaken only on a subpixel-by-subpixel basis). {\textcopyright} 2020 Elsevier Inc.",
keywords = "Accuracy assessment, Downscaling, Point spread function (PSF), Remote sensing images, Spectral unmixing, Subpixel mapping (SPM), Supper-resolution mapping, Function evaluation, Mapping, Pixels, Remote sensing, Restoration, Quantitative evaluation, Spatial structure, Sub-pixel classification, Sub-pixel mapping, Visual evaluation, Optical transfer function, accuracy assessment, digital mapping, downscaling, experimental study, image analysis, pixel, remote sensing",
author = "Q. Wang and C. Zhang and X. Tong and P.M. Atkinson",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Remote Sensing of Environment. 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 Remote Sensing of Environment, 251, 2020 DOI: 10.1016/j.rse.2020.112054",
year = "2020",
month = dec,
day = "15",
doi = "10.1016/j.rse.2020.112054",
language = "English",
volume = "251",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - General solution to reduce the point spread function effect in subpixel mapping

AU - Wang, Q.

AU - Zhang, C.

AU - Tong, X.

AU - Atkinson, P.M.

N1 - This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. 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 Remote Sensing of Environment, 251, 2020 DOI: 10.1016/j.rse.2020.112054

PY - 2020/12/15

Y1 - 2020/12/15

N2 - The point spread function (PSF) effect is ubiquitous in remote sensing images and imposes a fundamental uncertainty on subpixel mapping (SPM). The crucial PSF effect has been neglected in existing SPM methods. This paper proposes a general model to reduce the PSF effect in SPM. The model is applicable to any SPM methods treating spectral unmixing as pre-processing. To demonstrate the advantages of the new technique it was necessary to develop a new approach for accuracy assessment of SPM. To-date, accuracy assessment for SPM has been limited to subpixel classification accuracy, ignoring the performance of reproducing spatial structure in downscaling. In this paper, a new accuracy index is proposed which considers SPM performances in classification and restoration of spatial structure simultaneously. Experimental results show that by considering the PSF effect, more accurate SPM results were produced and small-sized patches and elongated features were restored more satisfactorily. Moreover, using the novel accuracy index, the quantitative evaluation was found to be more consistent with visual evaluation. This paper, thus, addresses directly two of the longest standing challenges in SPM (i.e., the limitations of the PSF effect and accuracy assessment undertaken only on a subpixel-by-subpixel basis). © 2020 Elsevier Inc.

AB - The point spread function (PSF) effect is ubiquitous in remote sensing images and imposes a fundamental uncertainty on subpixel mapping (SPM). The crucial PSF effect has been neglected in existing SPM methods. This paper proposes a general model to reduce the PSF effect in SPM. The model is applicable to any SPM methods treating spectral unmixing as pre-processing. To demonstrate the advantages of the new technique it was necessary to develop a new approach for accuracy assessment of SPM. To-date, accuracy assessment for SPM has been limited to subpixel classification accuracy, ignoring the performance of reproducing spatial structure in downscaling. In this paper, a new accuracy index is proposed which considers SPM performances in classification and restoration of spatial structure simultaneously. Experimental results show that by considering the PSF effect, more accurate SPM results were produced and small-sized patches and elongated features were restored more satisfactorily. Moreover, using the novel accuracy index, the quantitative evaluation was found to be more consistent with visual evaluation. This paper, thus, addresses directly two of the longest standing challenges in SPM (i.e., the limitations of the PSF effect and accuracy assessment undertaken only on a subpixel-by-subpixel basis). © 2020 Elsevier Inc.

KW - Accuracy assessment

KW - Downscaling

KW - Point spread function (PSF)

KW - Remote sensing images

KW - Spectral unmixing

KW - Subpixel mapping (SPM)

KW - Supper-resolution mapping

KW - Function evaluation

KW - Mapping

KW - Pixels

KW - Remote sensing

KW - Restoration

KW - Quantitative evaluation

KW - Spatial structure

KW - Sub-pixel classification

KW - Sub-pixel mapping

KW - Visual evaluation

KW - Optical transfer function

KW - accuracy assessment

KW - digital mapping

KW - downscaling

KW - experimental study

KW - image analysis

KW - pixel

KW - remote sensing

U2 - 10.1016/j.rse.2020.112054

DO - 10.1016/j.rse.2020.112054

M3 - Journal article

VL - 251

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

M1 - 112054

ER -