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@MastersThesis{Toniol:2017:UsDiCl,
               author = "Toniol, Alana Carla",
                title = "Uso de diferentes classificadores e de simula{\c{c}}{\~a}o 
                         estoc{\'a}stica para discrimina{\c{c}}{\~a}o de fitofisionomias 
                         do Cerrado usando atributos hiperespectrais do sensor 
                         Hyperion/EO-1",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2017",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2017-04-11",
             keywords = "Cerrado, classifica{\c{c}}{\~a}o, hiperespectral, 
                         simula{\c{c}}{\~a}o estoc{\'a}stica. Cerrado, classification, 
                         hyperspectral, stochastic simulation.",
             abstract = "O Cerrado brasileiro {\'e} considerado um dos mais importantes 
                         ecossistemas do mundo tanto pela riqueza de fauna quanto por sua 
                         ampla diversidade de esp{\'e}cies herb{\'a}ceas, arbustivas e 
                         arb{\'o}reas que ocorrem em um gradiente de vegeta{\c{c}}{\~a}o 
                         bem definido. Tendo em vista a import{\^a}ncia do monitoramento 
                         desse hotspot de biodiversidade, o sensoriamento remoto 
                         hiperespectral pode fornecer informa{\c{c}}{\~o}es sobre as 
                         caracter{\'{\i}}sticas biof{\'{\i}}sicas e 
                         bioqu{\'{\i}}micas de sua cobertura vegetal. O objetivo deste 
                         trabalho foi identificar o melhor conjunto de atributos 
                         hiperespectrais do sensor Hyperion/Earth Observing One (EO-1), 
                         testando o desempenho de diferentes t{\'e}cnicas de 
                         classifica{\c{c}}{\~a}o supervisionadas com esses atributos para 
                         discrimina{\c{c}}{\~a}o de fitofisionomias do Cerrado. Na etapa 
                         de classifica{\c{c}}{\~a}o foram consideradas duas imagens 
                         referentes {\`a} esta{\c{c}}{\~a}o chuvosa (13/01/2015) e seca 
                         (24/06/2015). A {\'a}rea de estudo foi o Parque Nacional de 
                         Bras{\'{\i}}lia (PNB). Os atributos testados foram: (a) a 
                         reflect{\^a}ncia de 146 bandas do sensor Hyperion; (b) a primeira 
                         derivada da reflect{\^a}ncia; (c) 22 {\'{\i}}ndices de 
                         vegeta{\c{c}}{\~a}o (IVs) de bandas estreitas; (d) a 
                         profundidade, {\'a}rea, largura e assimetria das bandas de 
                         absor{\c{c}}{\~a}o de clorofila em 680 nm; {\'a}gua foliar em 
                         980 e 1200 nm; lignina e celulose em 1700, 2100 e 2300 nm; e (e) 
                         todos os atributos em conjunto. Os classificadores testados foram 
                         {\'A}rvore de Decis{\~a}o J48 (AD), Random Forest (RF), Spectral 
                         Angle Mapper (SAM) e Support Vector Machine (SVM). Os resultados 
                         mostraram que a maior quantidade de atributos selecionados no 
                         per{\'{\i}}odo chuvoso compensou as confus{\~o}es espectrais 
                         associadas {\`a} estrutura da vegeta{\c{c}}{\~a}o durante esse 
                         per{\'{\i}}odo. Bandas mais profundas de absor{\c{c}}{\~a}o de 
                         {\'a}gua foram observadas no per{\'{\i}}odo chuvoso para as 
                         forma{\c{c}}{\~o}es arb{\'o}reas que apresentaram tamb{\'e}m 
                         maiores taxas de varia{\c{c}}{\~a}o espectral associadas {\`a} 
                         borda vermelha (primeira derivada). As classifica{\c{c}}{\~o}es 
                         do per{\'{\i}}odo chuvoso apresentaram desempenho levemente 
                         superior {\`a}s classifica{\c{c}}{\~o}es do per{\'{\i}}odo 
                         seco, especialmente para tipologias que inclu{\'{\i}}am 
                         esp{\'e}cies invasoras, embora a maioria das diferen{\c{c}}as em 
                         exatid{\~a}o de classifica{\c{c}}{\~a}o n{\~a}o tenham sido 
                         estatisticamente diferentes. As maiores exatid{\~o}es totais 
                         foram atribu{\'{\i}}das {\`a}s classifica{\c{c}}{\~o}es com 
                         todos os atributos em conjunto, enquanto que as menores 
                         exatid{\~o}es foram relacionadas aos atributos par{\^a}metros de 
                         bandas de absor{\c{c}}{\~a}o e derivada de 1\$^{}\$ ordem. 
                         Pelos mapas de entropia de Shannon e de moda, observou-se que as 
                         maiores incertezas entre os classificadores ocorreram para os 
                         atributos derivada de 1\$^{}\$ ordem e par{\^a}metros de 
                         bandas de absor{\c{c}}{\~a}o, estando associadas com as 
                         fitofisionomias campestres. Pelo processo de simula{\c{c}}{\~a}o 
                         estoc{\'a}stica foram confirmados os resultados obtidos pelos 
                         mapas de classifica{\c{c}}{\~a}o. Considerando um intervalo de 
                         credibilidade de 99\%, pode-se concluir que os melhores 
                         resultados de classifica{\c{c}}{\~a}o nos per{\'{\i}}odos 
                         chuvoso e seco foram observados para RF e SVM. Usando estes 
                         classificadores, as maiores percentagens de acerto de 
                         classifica{\c{c}}{\~a}o foram observadas com todos os atributos 
                         em conjunto para as forma{\c{c}}{\~o}es campestres e com IVs, 
                         reflect{\^a}ncia e todos os atributos para as 
                         forma{\c{c}}{\~o}es arb{\'o}reas. A utiliza{\c{c}}{\~a}o de 
                         simula{\c{c}}{\~a}o estoc{\'a}stica foi importante para a 
                         complementa{\c{c}}{\~a}o e confirma{\c{c}}{\~a}o dos 
                         resultados estat{\'{\i}}sticos associados aos processos de 
                         classifica{\c{c}}{\~a}o de imagens Hyperion. ABSTRACT: The 
                         Brazilian savanna, locally known as Cerrado, is one of the most 
                         important ecosystems of the world because of the high biodiversity 
                         of trees, shrubs and grasses associated with a well-defined 
                         vegetation gradient. In order to monitor this important world's 
                         hotspot, hyperspectral remote sensing can provide information on 
                         biophysical and biochemistry vegetation properties. The objective 
                         of this study was to identify the best set of hyperspectral 
                         attributes to be used as input to different classification 
                         techniques for discriminating the Cerrado physiognomies. In the 
                         classification phase, two images were considered in rainy season 
                         (01/13/2015) and dry (06/24/2015).The study area is the Parque 
                         Nacional de Bras{\'{\i}}lia (PNB). The attributes tested were, 
                         as follows: (a) the reflectance of 146 Hyperion bands; (b) the 
                         first-order derivative of reflectance; (c) 22 narrowband 
                         vegetation indices (VIs); (d) the depth, area, width and asymmetry 
                         of the 680-nm chlorophyll absorption band; the 980-nm and 1200-nm 
                         leaf water features; the 1700-nm, 2100-nm and 2300-nm 
                         lignin/cellulose absorption bands; and (e) all sets of attributes. 
                         The classifiers used in the data analysis were Decision Tree J48 
                         (DT), Random Forest (RF), Spectral Angle Mapper (SAM) and Support 
                         Vector Machine (SVM). The results showed that the greater spectral 
                         confusion in the rainy season than in the dry season was 
                         compensated by the selection of a greater number of hyperspectral 
                         attributes in the classification procedure. Deeper leaf water 
                         absorption bands were observed in the rainy season for the 
                         tree-wooded savannas, which showed also greater rates of 
                         reflectance changes in the red-edge interval (first-order 
                         derivative). Classification accuracy in the rainy season was 
                         slightly higher than in the dry season, especially for classes 
                         with invasive species, but most of the differences were not 
                         statistically significant. The highest classification accuracy was 
                         obtained with the use of all hyperspectral attributes, while the 
                         lowest values were noted for the absorption band parameters and 
                         first-order derivative of reflectance. These results were 
                         confirmed by the Shannon entropy and mode maps, which showed that 
                         the greatest uncertainties in the classification were associated 
                         with the grassland/shrub savanna physiognomies. From the 
                         stochastic simulation at 99\% confidence level, it was concluded 
                         that the best classification results in both seasons were observed 
                         for RF and SVM. Using these classifiers, the largest percentages 
                         of correct classification were obtained with all attributes for 
                         the grassland/shrub savannas and with reflectance, VIs and all 
                         attributes for the tree-wooded physiognomies. Overall, the 
                         stochastic simulation was important for complementing and 
                         confirming the statistical results associated with the 
                         classification of the Hyperion images.",
            committee = "Ponzoni, Fl{\'a}vio Jorge (presidente) and Galv{\~a}o, 
                         L{\^e}nio Soares (orientador) and Sanches, Ieda Del'Arco and 
                         Sano, Edson Eyji",
         englishtitle = "Use of different classifiers and stochastic simulation for the 
                         discrimination of Cerrado physiognomies using hyperspectral 
                         attributes of the Hyperion/EO-1 sensor.",
             language = "pt",
                pages = "144",
                  ibi = "8JMKD3MGP3W34P/3NNR6DB",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3NNR6DB",
           targetfile = "publicacao.pdf",
        urlaccessdate = "23 nov. 2020"
}


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