Aprendizado de máquina na detecção do uso do solo no bioma caatinga via sensoriamento remoto

• Ano de defesa: 2009
• Autor(a) principal: Sousa, Beatriz Fernandes Simplício
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Irrigação e drenagem; Inteligência artificial; Semiárido; Classificação de imagens de satélite; Artificial intelligence; Semiarid
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/17681

In order to manage adequately natural resources inside a fragile environment, just like Caatinga, one should know its properties and spatial distribution. This work proposes an approach to classify LANDSAT-5 satellite images. These images, corresponding to a semiarid environment located in Iguatu country, Ceara, Brazil, were classified aiming at detecting the Caatinga biome by two type of classifiers based on machinery learning: Multi Layer Perceptron (MLP) and Support Vector Machine (SVM). The static classifier of Maximum Likelihood was also used as comparison to the other two methods. Agriculture, water, anthropical, herbaceous shrub Caatinga (CHA) and dense high Caatinga (CAD) are the five classes defined for classifying. MLP method tests were carried out changing neurons quantity in the intermediate layer. SVM method tests were carried out changing σ, from Gauss function, and penalization parameter (C). Performance of the tests was analyzed by Global Accuracy, Specific Accuracy and Kappa coefficient. The last one calculated by confusion matrix, which has been generated by comparison of classification data and ground control points GPS georreferenced (true points). MLP method presented best performance for tests in which 12 neurons have been attributed to the intermediate layer resulting in Global Accuracy and Kappa values of 82.14% and 0.76, respectively. On the other hand, SVM method presented best performance for tests carried out with C=1000 and σ=2, resulting in Global Accuracy and Kappa values of 86.03% and 0.77, respectively. The Maximum Likelihood classifier presented 81.2% of its pixels correctly classified (Global Accuracy) and K coefficient value of 0.73. The values of Specific Accuracy, which makes it possible to analyze the performance of each individual class, were above 70% in each class. A total 576 km2 area was classified. Between the two types of Caatinga biome considered, herbaceous shrub Caatinga (CHA) comes to be the most common. Therefore, taking into account experimental results, it is possible to conclude that both SVM and MLP methods, which are based on machine learning, show satisfactory performance for classifying Caatinga biome.