Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived fro...

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Bibliographic Details
Main Authors: Andromachi Chatziantoniou, Emmanouil Psomiadis, George P. Petropoulos
Format: Article
Language:English
Published: MDPI AG 2017-12-01
Series:Remote Sensing
Subjects:
wetlands
Sentinel 1
Sentinel 2
Support Vector Machines
classification
SAR
Online Access:https://www.mdpi.com/2072-4292/9/12/1259

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