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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -