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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Bibliographic Details
Main Authors: Zainoolabadien Karim, Terence L. van Zyl
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:Remote Sensing
Subjects:
machine learning
deep learning
transfer learning
artificial intelligence
remote sensing
earth observation
Online Access:https://www.mdpi.com/2072-4292/13/21/4394

Internet

https://www.mdpi.com/2072-4292/13/21/4394

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