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Knowledge-Based Multispectral Remote Sensing Imagery Superresolution

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Reliability Engineering and Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 976))

Abstract

Software techniques for remotely sensed imagery superresolution enhance data reliability and veracity. The most common approach for superresolution is the processing of few images of the same scene captured simultaneously with subpixel shift relatively to each other. These conditions exclude radiometric inconsistency between images, and subpixel shift allow the extracting of additional land surface details. A general superresolution approach can be adopted to multispectral remote sensing imagery registered in different spectral bands. In this case, the intrinsic radiometric inconsistency can be overpassed by translating of the input bands into some additional virtual one, joint for all inputs. Typically, such an additional band overlaps all input ones in the spectrum. Necessary knowledge for bands translation are all bands spectral responses, as well as the subpixel shifts between restored images. So, the spectral radiance for a new spectral band is estimated. Therefore, each input band image transforms into new image in the same spectral range. Obtained images are appropriate for any existing superresolution techniques, for example, using Gaussian regularization in the frequency domain. The last step of the proposed method is image improvement after superresolution using a convolutional artificial neural network.

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Acknowledgements

This work was supported by the Slovak Research and Development Agency under the grant No. SK-SRB-18-0002.

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Authors and Affiliations

  1. Scientific Centre for Aerospace Research of the Earth, NAS of Ukraine, Kyiv, Ukraine

    Sergey A. Stankevich, Iryna O. Piestova, Mykola S. Lubskyi, Sergiy V. Shklyar, Artur R. Lysenko & Oleg V. Maslenko

  2. University of Žilina, Žilina, Slovakia

    Jan Rabcan

Authors
  1. Sergey A. Stankevich
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  2. Iryna O. Piestova
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  3. Mykola S. Lubskyi
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  4. Sergiy V. Shklyar
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  5. Artur R. Lysenko
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  6. Oleg V. Maslenko
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  7. Jan Rabcan
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Correspondence to Sergey A. Stankevich .

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Editors and Affiliations

  1. Healthy Living, TNO, Leiden, The Netherlands

    Coen van Gulijk

  2. Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia

    Elena Zaitseva

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Stankevich, S.A. et al. (2021). Knowledge-Based Multispectral Remote Sensing Imagery Superresolution. In: van Gulijk, C., Zaitseva, E. (eds) Reliability Engineering and Computational Intelligence. Studies in Computational Intelligence, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-74556-1_13

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