Application of Physical and Neural Network Methods in Operational Water Surface Detection

• Published in: Russian meteorology and hydrology Vol. 49; no. 4; pp. 328 - 335
• Main Author: Kuchma, M. O.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.04.2024; Springer Nature B.V
• Subjects: Algorithms; Atmospheric correction; Atmospheric effects; Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Electromagnetic radiation; Floods; Lookup tables; Meteorology; Neural networks; Preprocessing; Radiative transfer; Real time; River basins; Rivers; Russian spacecraft; Satellite data; Satellite instruments; Satellites; Solar spectrum; Spacecraft; Target detection; Amur River; river flood; mapping; convolutional neural network; MSU-MR low-resolution multispectral scanner; remote sensing; 6S radiative transfer model; atmospheric correction
• ISSN: 1068-3739; 1934-8096
• DOI: 10.3103/S106837392404006X

The paper presents some methods of satellite data preprocessing for the elimination of atmospheric effects on the electromagnetic radiation detected by the target equipment of a satellite and subsequent detection of floods in the Amur River basin. The atmospheric correction algorithm that has been used for the preprocessing is based on the use of a lookup table obtained by applying the Second Simulation of a Satellite Signal in the Solar Spectrum, which is a model of atmosphere radiative transfer. The subsequent flood detection in the Amur River basin water bodies builds on a neural network algorithm, the core of which is the upgraded U-Net. The developed algorithms for atmospheric correction and subsequent flood detection make it possible to receive information in an automatic near-real-time mode for monitoring flood conditions. Some groundwork has been made for applying the algorithm to the data of the Russian satellite instruments for spacecraft planned for launch.