author = "Costa, Wanderson Santos and Fonseca, Leila Maria Garcia and 
                         Korting, Thales Sehn",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Classifying grasslands and cultivated pastures in the brazilian 
                         cerrado using support vector machines, multilayer perceptrons and 
              journal = "Lecture Notes in Computer Science",
                 year = "2015",
               volume = "9166",
                pages = "187--198",
             keywords = "Autoenconder, Brazilian cerrado, Data mining, Image processing, 
                         Multilayer perceptron, Support vector machine.",
             abstract = "One of the most biodiverse regions on the planet, Cerrado is the 
                         second largest biome in Brazil. Among the land changes in the 
                         Cerrado, over 500,000km2 of the biome have been changed into 
                         cultivated pastures in recent years. Categorizing types of land 
                         cover and its native formations is important for protection policy 
                         and monitoring of the biome. Based on remote sensing techniques, 
                         this work aims at developing a methodology to map pasture and 
                         native grassland areas in the biome. Data related to EVI 
                         vegetation indices obtained by MODIS images were used to perform 
                         image classification. Support Vector Machine, Multilayer 
                         Perceptron and Autoencoder algorithms were used and the results 
                         showed that the analysis of different attributes extracted from 
                         EVI indices can aid in the classification process. The best result 
                         obtained an accuracy of 85.96% in the study area, identifying data 
                         and attributes required to map pasture and native grassland in 
                  doi = "10.1007/978-3-319-21024-7_13",
                  url = "http://dx.doi.org/10.1007/978-3-319-21024-7_13",
                 issn = "0302-9743",
             language = "en",
        urlaccessdate = "04 dez. 2020"