@InProceedings{MoreiraAlmCruFurSoa:2015:MoCoAp,
author = "Moreira, Mayne Assun{\c{c}}{\~a}o and Almeida, Paula Maria Moura
de and Cruz, Carla Bernadete Madureira and Furtado, Luiz Felipe de
Almeida and Soares, Mario Luiz Gomes",
affiliation = "{} and {} and {} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Modelagem do conhecimento aplicada {\`a} detec{\c{c}}{\~a}o de
mudan{\c{c}}as em ambiente costeiro",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "2023--2030",
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 landscape is constantly changing, be it natural or
anthropogenic character. Coastal environments are naturally more
dynamic than the inner portions of the continent, and lately has
been suffering with landscape changes by anthropogenic action.
Aiming at monitoring of these environments, the study of the
landscape changes has always been the target of numerous studies
of remote sensing. At the same time, the techniques used for such
analyzes has been constant improvement, however, a major challenge
is still analyzing large time series in such dynamic environment
as coastal areas. In this context, the present work was developed
in the pursuit of optimizing change detection techniques, without
losing the quality of the product generated. Using a historical
series of nine TM/Landsat 5 images, with 30 km resolution,
covering the period 1984-2006, and object-based images analysis, a
multiresolution segmentation of bands 3 and 4 each scene was done.
The classification of areas of change was made in two levels of
segmentation using mainly descriptors NDVI
(m{\'{\i}}n/m{\'a}x_NDVI and amp_NDVI). The result showed that
the optimization technique and the descriptors used were very
efficient for the separability of the classes not change and
change, with very good global accuracy (0.81) and Kappa index
(0.76) at 1: 150,000 scale, validated based reference points
collected in the field.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "401",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM49HL",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM49HL",
targetfile = "p0401.pdf",
type = "An{\'a}lise de s{\'e}ries de tempo de imagens de sat{\'e}lite",
urlaccessdate = "27 abr. 2024"
}