RORSE
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ALL-RUSSIA OPEN ANNUAL CONFERENCES ON

CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

Principal physics, methods and techniques for monitoring the environment, potentially dangerous phenomena and objects



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Information Technologies in Remote Sensing of the Earth - RORSE 2018

Proceedings of the 16th Conference (November 12-16, 2018, Moscow, Russia)

Development of the Recursive Method of Continuation of Spectra in the Implementation of Super-Resolution Using Images of the Spacecraft Constellation Model

Viktor N. Vintaev1, Mikhail Y. Gilenev2, Natalya N. Ushakova2

  1. Belgorod University of Cooperation, Economics and Law, Belgorod, Russia
    viktor.vn2010@yandex.ru, natush2006@yandex.ru
  2. AO Corporation «VNIIEM», Moscow, Russia
    zhilenev_mihail@bk.ru
DOI 10.21046/rorse2018.132
When implementing ultra-high resolution in the device grouping model, Kotelnikov's used theorem on image discretization and kernels of integral image deconvolution operators is aimed at spatial frequencies that lie far enough beyond the limits of transparency of the spatial frequency-contrast characteristic of the area-sensing virtual path. To further compensate for the loss of sharpness in the virtual channel due to dispersions in multiple overlay operations with images, the recursive method of extending the spatial-frequency spectra of images is implemented. The modified super-resolution method Iterative Back Projection for a group of images with a resolution of 1 m on the ground received patterns with a resolution of 0.5 m and further images were obtained with support for a resolution of 0.25 m (estimated by the Foucault method), which corresponds to the level of resolution for devices US intelligence. For a satellite with a resolution of 30 cm, support of 15 cm is obtained.
Keywords: super resolution, deconvolution, subpixel shift, grouping of devices, function singular at measure zero, Kotelnikov theorem, continuation of the spectrum, Iterative Back Projection method
References:

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Section 2. Methods and algorithms for processing remote monitoring data

132-138