@PhDThesis{Costa:2019:SeImSe,
author = "Costa, Wanderson Santos",
title = "Segmenta{\c{c}}{\~a}o de imagens de sensoriamento remoto baseada
em s{\'e}ries temporais e DTW",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2019",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2019-02-28",
keywords = "Segmenta{\c{c}}{\~a}o multitemporal, processamento de imagens,
sensoriamento remoto, dynamic time warping, multitemporal
segmentation, image processing, remote sensing, dynamic time
warping.",
abstract = "A disponibilidade de uma grande quantidade de dados de sensores
remotos com diferentes resolu{\c{c}}{\~o}es temporais e
espaciais tem tornado cada vez mais acess{\'{\i}}vel e detalhada
a observa{\c{c}}{\~a}o da Terra. Dentro deste contexto, o uso de
segmentadores eficientes em aplica{\c{c}}{\~o}es de
sensoriamento remoto apresenta um papel importante neste
cen{\'a}rio, ao buscar regi{\~o}es homog{\^e}neas no
dom{\'{\i}}nio espa{\c{c}}o-tempo e, consequentemente, reduzir
o conjunto de dados. Al{\'e}m disso, a segmenta{\c{c}}{\~a}o
multitemporal pode trazer uma nova maneira de
interpreta{\c{c}}{\~a}o dos dados, ao produzir regi{\~o}es
cont{\'{\i}}guas no tempo. Portanto, este trabalho descreve um
algoritmo de segmenta{\c{c}}{\~a}o multitemporal baseado em
s{\'e}ries temporais obtidas a partir de imagens {\'o}pticas de
sensoriamento remoto. A dist{\^a}ncia Dynamic Time Warping foi
utilizada como crit{\'e}rio de homogeneidade na
segmenta{\c{c}}{\~a}o e quatro estudos de caso foram realizados
para avaliar o m{\'e}todo proposto. Nesta avalia{\c{c}}{\~a}o
s{\~a}o usadas s{\'e}ries temporais de {\'{\i}}ndices de
vegeta{\c{c}}{\~a}o NDVI e EVI geradas a partir de imagens
MODIS, Landsat-8 e Landsat-7. Outros crit{\'e}rios de
homogeneidade foram avaliados. As avalia{\c{c}}{\~o}es
qualitativa e quantitativa demonstraram o potencial do m{\'e}todo
de segmenta{\c{c}}{\~a}o proposto. ABSTRACT: The availability of
a large amount of remote sensing data with different temporal and
spatial resolutions has increasingly made Earth observation more
accessible and detailed. In this context, the use of efficient
remote sensing image segmenters in remote sensing applications
plays an important role in this scenario when searching for
homogeneous regions in space-time domain and, consequently,
reducing the dataset. In addition, multitemporal segmentation can
bring a new way of interpreting data, producing contiguous regions
in time. Therefore, this thesis has the objective the development
of a multitemporal segmentation algorithm based on time series
from remote sensing optical images. The Dynamic Time Warping
distance was used as the homogeneity criterion and four case
studies were performed to evaluate the proposed method. In this
evaluation, time series of vegetation indices NDVI and EVI were
used, generated from MODIS, Landsat-8 and Landsat- 7 images. NDVI
and EVI vegetation indices from these sensors were used to create
the time series. Other homogeneity criteria were evaluated. The
qualitative and quantitative evaluations demonstrated the
potential of the proposed segmentation method.",
committee = "Santos, Rafael Duarte Coelho dos (presidente) and Fonseca, Leila
Maria Garcia (orientadora) and K{\"o}rting, Thales Sehn
(orientador) and Sant'Anna, Sidnei Jo{\~a}o Siqueira and Happ,
Patrick Nigri and Centeno, Jorge Antonio Silva",
englishtitle = "Segmentation of remote sensing images based on time series and
DTW",
language = "pt",
pages = "125",
ibi = "8JMKD3MGP3W34R/3T2SPDL",
url = "http://urlib.net/ibi/8JMKD3MGP3W34R/3T2SPDL",
targetfile = "publicacao.pdf",
urlaccessdate = "20 abr. 2024"
}