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Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data

Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. Thi...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Nikolaos Tziolas, Nikolaos Tsakiridis, Eyal Ben-Dor, John Theocharis, George Zalidis
Format: Artigo
Sprache:Inglês
Veröffentlicht: MDPI AG 2020-04-01
Schriftenreihe:Remote Sensing
Schlagworte:
deep learning
Copernicus data
soil texture mapping
earth observation
spectral signatures
SAR data
Online-Zugang:https://www.mdpi.com/2072-4292/12/9/1389
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