Detection of subtle thermal anomalies: Deep learning applied to the ASTER global volcano dataset

Twenty-one years of ASTER global thermal infrared (TIR) acquisitions provide a large amount of data for volcano monitoring. These data, with high spatial and spectral resolution, enable routine investigations of volcanoes in remote and inaccessible regions, including those with no ground-based monit...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
Hauptverfasser:	Corradino, Claudia, Ramsey, Michael S., Pailot-Bonnetat, Sophie, Harris, Andrew J.L., Negro, Ciro Del
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Anomalies Artificial neural networks ASTER (radiometer) Biology Convolutional neural networks Datasets Deep learning deep learning classifier Emissions Feature extraction Geology Machine learning Monitoring Neural networks Radiometers satellite remote sensing Sciences of the Universe Spatial data Spectral resolution Thermal analysis Thermal emission thermal infrared image data Thermal pollution Training Volcanic activity Volcanic eruptions Volcanoes
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