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A Geostatistical Filter for Remote Sensing Image Enhancement

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>1/03/2020
<mark>Journal</mark>Mathematical Geosciences
Number of pages20
Pages (from-to)317-336
Publication StatusPublished
Early online date10/10/19
<mark>Original language</mark>English


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.

Bibliographic note

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