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A Geostatistical Filter for Remote Sensing Image Enhancement

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A Geostatistical Filter for Remote Sensing Image Enhancement. / Wang, Qunming; Tong, Xiaohua; Atkinson, Peter M.
In: Mathematical Geosciences, Vol. 52, 01.03.2020, p. 317-336.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Wang Q, Tong X, Atkinson PM. A Geostatistical Filter for Remote Sensing Image Enhancement. Mathematical Geosciences. 2020 Mar 1;52:317-336. Epub 2019 Oct 10. doi: 10.1007/s11004-019-09829-1

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Wang, Qunming ; Tong, Xiaohua ; Atkinson, Peter M. / A Geostatistical Filter for Remote Sensing Image Enhancement. In: Mathematical Geosciences. 2020 ; Vol. 52. pp. 317-336.

Bibtex

@article{f73184f6b5f549c1b36ce4b5596cdc2a,
title = "A Geostatistical Filter for Remote Sensing Image Enhancement",
abstract = "In this paper, a new method was investigated to enhance remote sensing images by alleviating the point spread function (PSF) effect. The PSF effect exists ubiquitously in remotely sensed imagery. As a result, image quality is greatly affected, and this imposes a fundamental limit on the amount of information captured in remotely sensed images. A geostatistical filter was proposed to enhance image quality based on a downscaling-then-upscaling scheme. The difference between this method and previous methods is that the PSF is represented by breaking the pixel down into a series of sub-pixels, facilitating downscaling using the PSF and then upscaling using a square-wave response. Thus, the sub-pixels allow disaggregation as an attempt to remove the PSF effect. Experimental results on simulated and real data sets both suggest that the proposed filter can enhance the original images by reducing the PSF effect and quantify the extent to which this is possible. The predictions using the new method outperform the original coarse PSF-contaminated imagery as well as a benchmark method. The proposed method represents a new solution to compensate for the limitations introduced by remote sensors (i.e., hardware) using computer techniques (i.e., software). The method has widespread application value, particularly for applications based on remote sensing image analysis.",
keywords = "Geostatistics, Image enhancement, Image filtering, Point spread function (PSF), Remote sensing, Computer hardware, Image quality, Optical transfer function, Pixels, Amount of information, Computer techniques, Geo-statistics, Remote sensing images, Remotely sensed imagery, Remotely sensed images, Square-wave response",
author = "Qunming Wang and Xiaohua Tong and Atkinson, {Peter M.}",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s11004-019-09829-1",
year = "2020",
month = mar,
day = "1",
doi = "10.1007/s11004-019-09829-1",
language = "English",
volume = "52",
pages = "317--336",
journal = "Mathematical Geosciences",
issn = "1874-8961",
publisher = "Springer-Verlag",

}

RIS

TY - JOUR

T1 - A Geostatistical Filter for Remote Sensing Image Enhancement

AU - Wang, Qunming

AU - Tong, Xiaohua

AU - Atkinson, Peter M.

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s11004-019-09829-1

PY - 2020/3/1

Y1 - 2020/3/1

N2 - In this paper, a new method was investigated to enhance remote sensing images by alleviating the point spread function (PSF) effect. The PSF effect exists ubiquitously in remotely sensed imagery. As a result, image quality is greatly affected, and this imposes a fundamental limit on the amount of information captured in remotely sensed images. A geostatistical filter was proposed to enhance image quality based on a downscaling-then-upscaling scheme. The difference between this method and previous methods is that the PSF is represented by breaking the pixel down into a series of sub-pixels, facilitating downscaling using the PSF and then upscaling using a square-wave response. Thus, the sub-pixels allow disaggregation as an attempt to remove the PSF effect. Experimental results on simulated and real data sets both suggest that the proposed filter can enhance the original images by reducing the PSF effect and quantify the extent to which this is possible. The predictions using the new method outperform the original coarse PSF-contaminated imagery as well as a benchmark method. The proposed method represents a new solution to compensate for the limitations introduced by remote sensors (i.e., hardware) using computer techniques (i.e., software). The method has widespread application value, particularly for applications based on remote sensing image analysis.

AB - In this paper, a new method was investigated to enhance remote sensing images by alleviating the point spread function (PSF) effect. The PSF effect exists ubiquitously in remotely sensed imagery. As a result, image quality is greatly affected, and this imposes a fundamental limit on the amount of information captured in remotely sensed images. A geostatistical filter was proposed to enhance image quality based on a downscaling-then-upscaling scheme. The difference between this method and previous methods is that the PSF is represented by breaking the pixel down into a series of sub-pixels, facilitating downscaling using the PSF and then upscaling using a square-wave response. Thus, the sub-pixels allow disaggregation as an attempt to remove the PSF effect. Experimental results on simulated and real data sets both suggest that the proposed filter can enhance the original images by reducing the PSF effect and quantify the extent to which this is possible. The predictions using the new method outperform the original coarse PSF-contaminated imagery as well as a benchmark method. The proposed method represents a new solution to compensate for the limitations introduced by remote sensors (i.e., hardware) using computer techniques (i.e., software). The method has widespread application value, particularly for applications based on remote sensing image analysis.

KW - Geostatistics

KW - Image enhancement

KW - Image filtering

KW - Point spread function (PSF)

KW - Remote sensing

KW - Computer hardware

KW - Image quality

KW - Optical transfer function

KW - Pixels

KW - Amount of information

KW - Computer techniques

KW - Geo-statistics

KW - Remote sensing images

KW - Remotely sensed imagery

KW - Remotely sensed images

KW - Square-wave response

U2 - 10.1007/s11004-019-09829-1

DO - 10.1007/s11004-019-09829-1

M3 - Journal article

VL - 52

SP - 317

EP - 336

JO - Mathematical Geosciences

JF - Mathematical Geosciences

SN - 1874-8961

ER -