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On Variational Problem with Nonstandard Growth Conditions for the Restoration of Clouds Corrupted Satellite Images

MPG-Autoren
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Khanenko,  Pavel
Physics of Quantum Materials, Max Planck Institute for Chemical Physics of Solids, Max Planck Society;

Uvarov,  M.
Physics of Quantum Materials, Max Planck Institute for Chemical Physics of Solids, Max Planck Society;

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Zitation

Khanenko, P., Kogut, P., & Uvarov, M. (2022). On Variational Problem with Nonstandard Growth Conditions for the Restoration of Clouds Corrupted Satellite Images. In CITRisk 2021 Computational & Information Technologies for Risk-Informed Systems 2021 Proceedings of the 2nd International Workshop on Computational & Information Technologies for Risk-Informed Systems (CITRisk 2021) co-located with XXI International Conference on Information Technologies in Education and Management (ITEM 2021) Kherson, Ukraine, September 16-17, 2021. CEUR workshop proceedings; 3101 (pp. 6-25). Technical University of Aachen. Retrieved from http://ceur-ws.org/Vol-3101/Paper1.pdf.


Zitierlink: https://hdl.handle.net/21.11116/0000-000A-A8D0-D
Zusammenfassung
Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. Typically, the optical satellite images are often corrupted because of poor weather conditions. As a rule, the measure of degradation of optical images is such that one can not rely even on the brightness inside of the damaged regions. As a result, some subdomains of such images become absolutely invisible. So, there is a risk of information loss in optical remote sensing data. In view of this, we propose a new variational approach for exact restoration of multispectral satellite optical images. We discuss the consistency of the proposed variational model, give the scheme for its regularization, derive the corresponding optimality system, and discuss the algorithm for the practical implementation of the reconstruction procedure. Experimental results are very promising and they show a significant gain over baseline methods using the reconstruction through linear interpolation between data available at temporally-close time instants. © 2021 Copyright for this paper by its authors.