@InProceedings{NegriDutrMend:2015:ApMéFu,
author = "Negri, Rog{\'e}rio Galante and Dutra, Luciano Vieira and Mendes,
Tatiana Sussel Gon{\c{c}}alves",
affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Aplica{\c{c}}{\~a}o de um m{\'e}todo fundamentado em grafo e
dist{\^a}ncias estoc{\'a}sticas na classifica{\c{c}}{\~a}o
baseada em regi{\~o}es",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "3400--3406",
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 = "Traditionally, classification of remote sensing images has been
performed using only the spectral pixels'' information through the
so-called Pixel Based Methods. However, there are cases which the
pixel based approach is not able to provide adequate results. An
alternative is the use of Region Based Classification. The region
based classification can be performed in different ways, for
example, by methods based on minimum distance or Support Vector
Machine (SVM). This work investigated the application of Graph
Classification for region based classification. A case study about
the land use and land cover classification on an Amazon region
adopting the SPOT image was conducted. The comparison made between
Graph Classification, SVM and the Minimum Distance show that Graph
Classification achieves better results.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "674",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM4BLF",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4BLF",
targetfile = "p0674.pdf",
type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
urlaccessdate = "27 abr. 2024"
}