@InCollection{SantosSiFeQuCaSa:2017:ClMeAs,
author = "Santos, Lorena Alves dos and Sim{\~o}es, Rolf Ezequiel de
Oliveira and Ferreira, Karine Reis and Queiroz, Gilberto Ribeiro
and Camara, Gilberto and Santos, Rafael Duarte Coelho dos",
title = "Clustering methods to asses land cover samples of modis vegetation
indexes time series",
booktitle = "Computational Science and Its Applications – ICCSA 2017",
publisher = "Springer",
year = "2017",
editor = "Gervasi, Osvaldo and Murgante, Beniamino and Misra, Sanjay and
Borruso, Giuseppe and Torre, Carmelo M. and Rocha, Ana Maria A. C.
and Taniar, David and Apduhan, Bernady O. and Stankova, Elena and
Cuzzocrea, Alfredo",
pages = "662--673",
keywords = "Time series clustering, MODIS vegetation indexes, Land cover
change classification, Self-Organizing Map (SOM).",
abstract = "MODIS vegetation indexes time series have been widely used to
build land cover change maps on large scales. In this scope, to
obtain good quality maps using supervised classification methods,
it is crucial to select representative training samples of land
cover change classes. In this paper, we evaluate two clustering
methods, Hierarchical and Self-Organizing Map (SOM), to assess
land cover samples of MODIS vegetation indexes time series. As we
show, these techniques are suitable tools for assisting users to
select representative land cover change samples from MODIS
vegetation indexes time series. We present the accuracy of both
methods for a case study in Ipiranga do Norte municipality in Mato
Grosso state, Brazil.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
isbn = "978-331962406-8",
language = "en",
seriestitle = "Lecture Notes in Computer Science , 10409",
urlaccessdate = "21 maio 2024"
}