@Article{CostaFonsKort:2015:ClGrCu,
author = "Costa, Wanderson Santos and Fonseca, Leila Maria Garcia and
Korting, Thales Sehn",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Classifying grasslands and cultivated pastures in the brazilian
cerrado using support vector machines, multilayer perceptrons and
autoencoders",
journal = "Lecture Notes in Computer Science",
year = "2015",
volume = "9166",
pages = "187--198",
keywords = "Autoenconder, Brazilian cerrado, Data mining, Image processing,
Multilayer perceptron, Support vector machine.",
abstract = "One of the most biodiverse regions on the planet, Cerrado is the
second largest biome in Brazil. Among the land changes in the
Cerrado, over 500,000km2 of the biome have been changed into
cultivated pastures in recent years. Categorizing types of land
cover and its native formations is important for protection policy
and monitoring of the biome. Based on remote sensing techniques,
this work aims at developing a methodology to map pasture and
native grassland areas in the biome. Data related to EVI
vegetation indices obtained by MODIS images were used to perform
image classification. Support Vector Machine, Multilayer
Perceptron and Autoencoder algorithms were used and the results
showed that the analysis of different attributes extracted from
EVI indices can aid in the classification process. The best result
obtained an accuracy of 85.96% in the study area, identifying data
and attributes required to map pasture and native grassland in
Cerrado.",
doi = "10.1007/978-3-319-21024-7_13",
url = "http://dx.doi.org/10.1007/978-3-319-21024-7_13",
issn = "0302-9743",
language = "en",
urlaccessdate = "27 abr. 2024"
}