@InProceedings{AndradeFranAlme:2015:DeClPa,
author = "Andrade, Alexandre Curvelo de and Francisco, Cristiane Nunes and
Almeida, Cl{\'a}udia Maria de",
affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Desempenho de classificadores param{\'e}trico e n{\~a}o
param{\'e}trico na classifica{\c{c}}{\~a}o da fisionomia
vegetal",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "7611--7618",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "The present work is committed to conduct a comparative analysis
between two supervised classifiers, Maximum Likelihood and Support
Vector Machine, respectively parametric and non-parametric, for
the classification of vegetal physiognomies using very high
spatial resolution imagery, emphasizing the gain in performance
with the accordingly increase in the number of attributes. The
database consisted of pan-sharpened QuickBird images and
transformed images derived from the original bands besides relief
data obtained from the TOPODATA Project. The study area extends
over a surface of 16 km2 and is located within the municipality of
Nova Friburgo, in the mountainous region of Rio de Janeiro state.
In total, four experiments were accomplished all of them combining
the adopted classifier with a different number of attributes. In
the first two experiments, only the four QuickBird spectral bands,
previously subject to geometric and radiometric corrections, were
used. In the remainder two experiments, eighteen input bands were
employed. The Kappa indices obtained with the Maximum Likelihood
classifier lied between 0.64 and 0.66, while those obtained for
the Support Vector Machine ranged from 0.52 to 0.80. Considering
the attained results, we concluded that the number of input bands
does not meaningfully increase the accuracy of the Maximum
Likelihood classifier, whereas this factor greatly influences the
Support Vector Machine performance.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "1750",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM4K9E",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4K9E",
targetfile = "p1750.pdf",
type = "Processamento de imagens",
urlaccessdate = "28 abr. 2024"
}