Land use classification from Sentinel-2 imagery

Authors

  • J. Borràs Universitat de València
  • J. Delegido Universitat de València
  • A. Pezzola Instituto Nacional de Tecnología Agropecuaria
  • M. Pereira-Sandoval Universitat de València
  • G. Morassi Universitat de València
  • G. Camps-Valls Universitat de València

DOI:

https://doi.org/10.4995/raet.2017.7133

Keywords:

classification, remote sensing, land use, Kappa Index, Sentinel-2

Abstract

Sentinel-2 (S2), a new ESA satellite for Earth observation, accounts with 13 bands which provide high-quality radiometric images with an excellent spatial resolution (10 and 20 m) ideal for classification purposes. In this paper, two objectives have been addressed: to determine the best classification method for S2, and to quantify its improvement with respect to the SPOT operational mission. To do so, four classifiers (LDA, RF, Decision Trees, K-NN) have been selected and applied to two different agricultural areas located in Valencia (Spain) and Buenos Aires (Argentina). All classifiers were tested using, on the one hand, all the S2 bands and, on the other hand, only selecting those bands from S2 closer to the four bands from SPOT. In all the cases, between 10%-50% of samples were used to train the classifier while remaining the rest for validation. As a result, a land use map was generated from the best classifier, according to the Kappa index, providing scientifically relevant information such as the area of each land use class.

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Author Biographies

J. Borràs, Universitat de València

Laboratorio de Procesado de Imágenes

J. Delegido, Universitat de València

Laboratorio de Procesado de Imágenes

A. Pezzola, Instituto Nacional de Tecnología Agropecuaria

Estación Experimental Hilario Ascasubi. Laboratorio de Teledetección y SIG

M. Pereira-Sandoval, Universitat de València

Laboratorio de Procesado de Imágenes

G. Morassi, Universitat de València

Laboratorio de Procesado de Imágenes

G. Camps-Valls, Universitat de València

Laboratorio de Procesado de Imágenes

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Published

2017-06-20

Issue

Section

Research articles