Sentinel-1 SAR Images and Deep Learning for Water Body Mapping
نویسندگان
چکیده
Floods occur throughout the world and are becoming increasingly frequent dangerous. This is due to different factors, among which climate change land use stand out. In Mexico, they every year in areas. Tabasco a periodically flooded region, causing losses negative consequences for rural, urban, livestock, agricultural, service industries. Consequently, it necessary create strategies intervene effectively affected Different techniques have been developed mitigate damage caused by this phenomenon. Satellite programs provide large amount of data on Earth’s surface geospatial information processing tools useful environmental forest monitoring, impacts, risk analysis, natural disasters. paper presents strategy classification areas using satellite images obtained from synthetic aperture radar, as well U-Net neural network ArcGIS platform. The study area located Los Rios, region Tabasco, Mexico. results show that performs despite limited number training samples. As epochs increase, its precision increases.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15123009