Automatic Detection of Flooded Areas in Polarimetric Radar Images From the Sentinel-1 Satellite

Nadiane Nguekeu Metepong Lagpong, Joseph Mvogo Ngono, Auguste Vigny Noumsi Woguia, Pierre Ele and Adrien Arnaud Kemche Ghomsi
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Nadiane Nguekeu Metepong Lagpong: University of Douala, Cameroon
Joseph Mvogo Ngono: University of Douala, Cameroon
Auguste Vigny Noumsi Woguia: University of Douala, Cameroon
Pierre Ele: University of Yaounde 1, Cameroon
Adrien Arnaud Kemche Ghomsi: University of Douala, Cameroon

International Journal of Applied Geospatial Research (IJAGR), 2025, vol. 16, issue 1, 1-17

Abstract: The free availability of Synthetic Aperture Radar (SAR) data from the sentinel satellite offers a unique opportunity for developing countries. The research work focuses on the floods in the town of Yagoua. The choice of this area is based on the multitude of floods causing enormous damage. Existing methods, primarily based on machine learning and deep learning algorithms, present major limitations such as sensitivity to radar noise, algorithmic complexity, and dependency on training data. The methodology proposed here uses the Kolmogorov algebraic method algorithm, which will be applied to the pre-processed images. The Fuzzy C-Means algorithm will then be used to generate a change map consisting of two output classes (water and not water). coupling these two methods gives good results and analysis of pre- and post-flood images resulted in an average improvement of 12% compared to state-of-the-art methods. This approach enhances rapid and reliable flood monitoring.

Date: 2025
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