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@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/rep/8JMKD3MGP3W34R/3T2SPDL",
           targetfile = "publicacao.pdf",
        urlaccessdate = "02 dez. 2020"
}


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