Deep/Transfer Learning with Feature Space Ensemble Networks (FeatSpaceEnsNets) and Average Ensemble Networks (AvgEnsNets) for Change Detection Using DInSAR Sentinel-1 and Optical Sentinel-2 Satellite Data Fusion

Deep/Transfer Learning with Feature Space Ensemble Networks (FeatSpaceEnsNets) and Average Ensemble Networks (AvgEnsNets) for Change Detection Using DInSAR Sentinel-1 and Optical Sentinel-2 Satellite Data Fusion

Differential interferometric synthetic aperture radar (DInSAR), coherence, phase, and displacement are derived from processing SAR images to monitor geological phenomena and urban change. Previously, Sentinel-1 SAR data combined with Sentinel-2 optical imagery has improved classification accuracy in...

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Autores principales: Zainoolabadien Karim, Terence L. van Zyl
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
machine learning
deep learning
transfer learning
artificial intelligence
remote sensing
earth observation
Science
Q
Acceso en línea:https://doaj.org/article/375fcd9d882c4ff7a8e83c3f7d15e68f
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https://doaj.org/article/375fcd9d882c4ff7a8e83c3f7d15e68f

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