Resumo

PECH-MAY, Fernando et al. Flooded Areas Detection through SAR Images and U-NET Deep Learning Model. Comp. y Sist. [online]. 2023, vol.27, n.2, pp.449-458.  Epub 18-Set-2023. ISSN 2007-9737.  https://doi.org/10.13053/cys-27-2-4624.

Floods are common in much of the world, this is due to different factors among which climate change and land use stand out. In Mexico they happen every year in different entities. Tabasco is an entity that is periodically flooded, causing losses and negative consequences for the rural, urban, livestock, agricultural and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected áreas. Therefore, different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the earth’s surface as well as geospatial information processing tools that are useful for environmental and forest monitoring, climate change impacts, risk analysis, natural disasters, among others. This paper presents a strategy for the classification of flooded áreas using satellite images radar of synthetic aperture and the U-NET neural network. The study área is centered on Los Ríos, region of Tabasco, Mexico. The partial results show that U-NET performs well despite the limited amount in the training samples. As training data and epochs increased, its accuracy increased.

Palavras-chave : Deep learning and SAR; sentinel-1 SAR; flood detection.