Detection of Forest Fires through Deep Unsupervised Learning Modeling of Sentinel-1 Time Series

Detection of Forest Fires through Deep Unsupervised Learning Modeling of Sentinel-1 Time Series

With an increase in the amount of natural disasters, the combined use of cloud-penetrating Synthetic Aperture Radar and deep learning becomes unavoidable for their monitoring. This article proposes a methodology for forest fire detection using unsupervised location-expert autoencoders and Sentinel-1...

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Bibliographic Details
Main Authors: Thomas Di Martino, Bertrand Le Saux, Régis Guinvarc’h, Laetitia Thirion-Lefevre, Elise Colin
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Synthetic Aperture Radar
forest monitoring
unsupervised learning
remote sensing time series
fire detection
Online Access:https://www.mdpi.com/2220-9964/12/8/332

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