@Article{SousaTeiSilAndBra:2010:AvClBa,
               author = "Sousa, Beatriz Fernandes Simplicio and Teixeira, Adunias dos 
                         Santos and Silva, Francisco de Assis Tavares Ferreira da and 
                         Andrade, Eunice Maia de and Braga, Arthur Pl{\'{\i}}nio de 
                         Souza",
          affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Avalia{\c{c}}{\~a}o de classificadores baseados em aprendizado 
                         de m{\'a}quina para a classifica{\c{c}}{\~a}o do uso e 
                         cobertura da terra no bioma caatinga",
              journal = "Revista Brasileira de Cartografia",
                 year = "2010",
               volume = "62",
               number = "2 , setembro 2010.",
                pages = "edi{\c{c}}{\~a}o Especial",
                month = "set.",
             keywords = "Intelig{\^e}ncia Artificial, Semi-{\'a}rido, 
                         Classifica{\c{c}}{\~a}o de Imagens de Sat{\'e}lite, Artificial 
                         Intelligence, Semi Arid, Satellite Image Classification.",
             abstract = "O manejo adequado dos recursos naturais em ambientes fr{\'a}geis, 
                         como o da Caatinga, requer o conhecimento de suas propriedades e 
                         distribui{\c{c}}{\~a}o espacial. Nesse contexto, o trabalho tem 
                         por objetivo avaliar o desempenho de dois algoritmos baseados em 
                         aprendizado de m{\'a}quina (Multi Layer Perceptron (MLP) e o 
                         Support Vector Machine (SVM)) e do m{\'e}todo da M{\'a}xima 
                         Verossimilhan{\c{c}}a na classifica{\c{c}}{\~a}o do uso e 
                         cobertura da terra no bioma Caatinga. Para o experimento, foi 
                         utilizada uma imagem do sat{\'e}lite LANDSAT-5/TM contendo a 
                         {\'a}rea de estudo localizada no munic{\'{\i}}pio de Iguatu-CE 
                         e definidas as classes de cobertura da terra, a saber: 
                         antropiza{\c{c}}{\~a}o por agricultura (APA), outros tipos de 
                         antropiza{\c{c}}{\~a}o (OTA), {\'a}gua, caatinga herb{\'a}cea 
                         arbustiva (CHA) e caatinga arb{\'o}rea densa (CAD). O desempenho 
                         dos m{\'e}todos foi analisado atrav{\'e}s dos coeficientes de 
                         Exatid{\~a}o Global (EG), Exatid{\~a}o Espec{\'{\i}}fica (EE) 
                         e Kappa (K) calculados a partir dos dados da matriz de 
                         confus{\~a}o correspondente {\`a} verdade terrestre. Os valores 
                         do coeficiente de EG foram de: 86,03%, 82,14% e 81,2% e K de: 
                         0,77, 0,76 e 0,75 nos m{\'e}todos SVM, MLP e M{\'a}xima 
                         Verossimilhan{\c{c}}a, respectivamente. Os valores de EE foram 
                         superiores a 70% para todos os classificadores testados. Os 
                         resultados obtidos demonstram que os m{\'e}todos SVM e MLP 
                         est{\~a}o aptos {\`a} classifica{\c{c}}{\~a}o dos padr{\~o}es 
                         propostos, j{\'a} que apresentaram resultados semelhantes ao 
                         m{\'e}todo tradicional da M{\'a}xima Verossimilhan{\c{c}}a. 
                         Por{\'e}m, estes classificadores podem consumir mais tempo na 
                         etapa de defini{\c{c}}{\~a}o dos par{\^a}metros da rede e de 
                         processamento.ABSTRACT Proper management of natural resources in 
                         fragile environments, such as the Caatinga, requires knowledge of 
                         their properties and spatial distribution. In this context, the 
                         study aims at evaluating the performance of two algorithms based 
                         on machine learning (Multi Layer Perceptron (MLP) and Support 
                         Vector Machine (SVM)) and the Maximum Proper management of natural 
                         resources in fragile environments, such as the Caatinga, requires 
                         knowledge of their properties and spatial distribution. In this 
                         context, the study aims at evaluating the performance of two 
                         algorithms based on machine learning (Multi Layer Perceptron (MLP) 
                         and Support Vector Machine (SVM)) and the Maximum Likelihood 
                         method to classify land use and land cover in the Caatinga biome. 
                         For the experiment, it was used a satellite image of LANDSAT-5/TM 
                         containing the study area located in the municipality of 
                         Iguatu-CE, and classes of land cover, namely: anthropized by 
                         agriculture, other types of anthropized, water, herbaceous shrub 
                         savanna (CHA ) and dense arboreal savanna (CAD) were defined. The 
                         performance of the methods was analyzed by the coefficient of 
                         Global Accuracy (EG), Accuracy Specific (EE) and Kappa (K) 
                         coefficient calculated with data taken from the confusion matrix 
                         corresponding to ground truth. The coefficient of EG were: 86.03%, 
                         82.14% and 81.2% and K: 0.77, 0.76 and 0.75 in the methods SVM, 
                         MLP and maximum likelihood respectively. EE values were above 70% 
                         for all classifiers tested. The results have shown that SVM and 
                         MLP methods are suited to the classification of the proposed 
                         standards, as it showed similar results to the traditional method 
                         of maximum likelihood. However, these methods are more time 
                         consuming in the stage of defining the parameters of the network 
                         and may require more computation power during stage of 
                         processing.",
                 issn = "0560-4613 and 1808-0936",
             language = "pt",
           targetfile = "62_ESPECIAL_02_6.pdf",
        urlaccessdate = "23 maio 2024"
}