@MastersThesis{Ibaņez:2016:UsReNe,
author = "Ibaņez, Marilyn Menecucci",
title = "Uso de redes neurais nebulosas e florestas aleat{\'o}rias na
classifica{\c{c}}{\~a}o de imagens em um projeto de ci{\^e}ncia
cidad{\~a}",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2016",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2016-02-19",
keywords = "redes neurais, florestas aleat{\'o}rias, processamento de
imagens, computa{\c{c}}{\~a}o cidad{\~a}, desmatamento,
sat{\'e}lites, neural network, image processing, computing
citizen, desforestation, satellites.",
abstract = "Recentemente, um projeto de ci{\^e}ncias cidad{\~a} chamado
\emph{ForestWatchers} (LUZ et al., 2014) foi lan{\c{c}}ado com o
objetivo de envolver os cidad{\~a}os leigos no monitoramento do
desmatamento. Por meio de uma interface Web, volunt{\'a}rios de
todo o mundo s{\~a}o convidados a analisar imagens MODIS de
regi{\~o}es florestais e confirmar se atribui{\c{c}}{\~o}es
autom{\'a}ticas de regi{\~o}es de florestas desmatadas
est{\~a}o corretamente classificadas. Considerando a grande
{\'a}rea em todo mundo coberta pelas florestas tropicais,
torna-se fundamental o uso de um classificador r{\'a}pido que
atenda a um objetivo duplo: o mapeamento de pixels em duas classes
(\${'}\$Floresta\${'}\$ e \${'}\$n{\~a}o-Floresta\${'}\$)
e a sele{\c{c}}{\~a}o dos pixels a serem enviados aos
volunt{\'a}rios para a inspe{\c{c}}{\~a}o, com base em uma
m{\'e}trica de confian{\c{c}}a. Nesta disserta{\c{c}}{\~a}o
investiga-se o uso de dois m{\'e}todos distintos - rede neural de
perceptrons multicamada (\emph{Multi-Layered Perceptron}, MLP)
difusa e Floresta Aleat{\'o}ria (\emph{Random Forest, RF}) - na
classifica{\c{c}}{\~a}o de padr{\~o}es de desmatamento na
Amaz{\^o}nia brasileira, utilizando imagens MODIS. Neste sentido,
foram gerados mapas de desmatamento de diversos tamanhos, de
diversas {\'a}reas do estado de Rond{\^o}nia. Os resultados
foram validados com os resultados de projeto PRODES, que avalia
anualmente o desmatamento na Amaz{\^o}nia brasileira. Nestes
testes, o classificador RF apresentou um desempenho amplamente
superior ao das redes neurais \emph{Multi-Layered Perceptro e
Multi-Layered Perceptron Fuzzy}. ABSTRACT: Recently, a citizen
science project called ForestWatchers (LUZ et al., 2014) was
launched in order to involve the laity citizens in the monitoring
of deforestation. Through a Web interface, volunteers from around
the world are invited to review MODIS images of forest regions and
confirm that automatic assignment of cleared forest areas are
properly classified. Considering the large area worldwide covered
by tropical forest, it is essential to use a fast classifier that
meets a double objective: the pixel mapping into two classes
(\${'}\$Forest\${'}\$ and \${'}\$non-forest\${'}\$) and
the selection of pixels to be sent to volunteers for inspection,
based on a reliable metric. This dissertation investigates the use
of two different methods - neural network multilayer perceptrons
(Multi-Layered Perceptron, MLP) diffuse and Random Forest (Random
Forest, RF) - the deforestation pattern classification in the
Brazilian Amazon using MODIS images. In this sense, deforestation
maps were generated from various sizes, from different areas of
the state of Rondonia. The results were validated with the results
of PRODES project, which annually evaluates deforestation in the
Brazilian Amazon. In these tests, the classifier RF showed a
vastly superior performance to the \emph{Multi-Layered Perceptro
and Multi-Layered Perceptron Fuzzy neural networks.}.",
committee = "Rosa, Reinaldo Roberto (presidente) and Ramos, Fernando Manuel
(orientador) and Carvalho, Adenilson Roberto (orientador) and
Becceneri, Jos{\'e} Carlos and Shiguemori, Elcio Hideiti",
copyholder = "SID/SCD",
englishtitle = "Use of fuzzy neural networks and random forest in image's
classification of a citizen science project",
language = "pt",
pages = "120",
ibi = "8JMKD3MGP3W34P/3L2D5ME",
url = "http://urlib.net/ibi/8JMKD3MGP3W34P/3L2D5ME",
targetfile = "publicacao.pdf",
urlaccessdate = "30 abr. 2024"
}