A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forest...

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Bibliographic Details
Main Authors: Flávio F. Camargo, Edson E. Sano, Cláudia M. Almeida, José C. Mura, Tati Almeida
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Remote Sensing
Subjects:
SAR
polarimetry
data mining
thematic mapping
Cerrado
Online Access:https://www.mdpi.com/2072-4292/11/13/1600

Internet

https://www.mdpi.com/2072-4292/11/13/1600

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