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Volume: 66 | Article ID: 050502
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Non-uniformity Correction Method of Remote Sensing Images based on Adaptive Moving Window Moment Matching
  DOI :  10.2352/J.ImagingSci.Technol.2022.66.5.050502  Published OnlineSeptember 2022
Abstract
Abstract

To improve the quality of optical remote sensing images, this paper proposes an adaptive moving window moment matching method for non-uniformity correction of remote sensing images. First, the scene complexity of the remote sensing image to be corrected is calculated, and the range of the width of the moving window is determined accordingly; Subsequently, the local scene complexity of the image in the window centered on the column to be corrected is calculated, and the width of the moving window is controlled adaptively; the correction gain and offset values of the column to be corrected of the image in the moving window are then calculated, and the correction of the columns to be corrected is performed accordingly; finally, the whole remote sensing image is traversed, and moment matching correction is performed column by column, and the correction of the non-uniformity of the whole remote sensing image is completed. The experimental results show that the non-uniformity correction effect of this algorithm is better than other competing algorithms, and the mean value of non-uniformity coefficient of the corrected image is reduced by more than 0.1802% compared with the minimum value in other comparison algorithms, which effectively eliminates the streak and stripe noise in the image, and better preserves the detailed information of the image.

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  Cite this article 

Junhua Yan, Yufeng Kang, Yue Ni, Yin Zhang, Junjie Fan, Xutong Hu, "Non-uniformity Correction Method of Remote Sensing Images based on Adaptive Moving Window Moment Matchingin Journal of Imaging Science and Technology,  2022,  pp 050502-1 - 050502-14,  https://doi.org/10.2352/J.ImagingSci.Technol.2022.66.5.050502

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Copyright © Society for Imaging Science and Technology 2022
  Article timeline 
  • received October 2021
  • accepted June 2022
  • PublishedSeptember 2022

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