%0 Conference Proceedings
%4 dpi.inpe.br/marte/2011/07.25.17.27
%2 dpi.inpe.br/marte/2011/07.25.17.27.12
%@isbn 978-85-17-00056-0 (Internet)
%@isbn 978-85-17-00057-7 (DVD)
%T Avaliação do potencial de classificadores automáticos para mapeamento de uso e cobertura do solo sob manejo agroecológico
%D 2011
%A Portes, Raquel de Castro,
%A Scudeller, Alice Azevedo,
%A Jusksch, Ivo,
%A Fernandes Filho, Elpídio Inácio,
%A Cardoso, Irene Maria,
%A Gleriani, José Marinaldo,
%@affiliation Universidade Federal de Viçosa – UFV
%@affiliation Universidade Federal de Viçosa – UFV
%@affiliation Universidade Federal de Viçosa – UFV
%@affiliation Universidade Federal de Viçosa – UFV
%@affiliation Universidade Federal de Viçosa – UFV
%@affiliation Universidade Federal de Viçosa – UFV
%@electronicmailaddress raquel_portes@yahoo.com.br
%@electronicmailaddress ascudeller@gmail.com
%@electronicmailaddress ivo@ufv.br
%@electronicmailaddress elpidio@ufv.br
%@electronicmailaddress irene@ufv.br
%@electronicmailaddress gleriani@ufv.br
%E Epiphanio, José Carlos Neves,
%E Galvão, Lênio Soares,
%B Simpósio Brasileiro de Sensoriamento Remoto, 15 (SBSR).
%C Curitiba
%8 30 abr. - 5 maio 2011
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 576-583
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%K Remote Sensing, Maximum Likelihood, Artificial Neural Netwoks, SAFs, IKONOS, sensoriamento remoto, Máxima verossimilhança, redes neurais artificiais e Bhattacharya.
%X The production areas based in agroecology systems are being implemented in Brazil and present on small farms intercropping different species of plants and making the diverse agricultural landscape. The classification of land cover and soil of these areas requires the use of images with high spatial resolution for detailed mapping and to identify the best method to rank areas with heterogeneous patterns of use. This study aimed to evaluate the potential of automatic classifiers for mapping land cover and soil under agro-ecological management in the São Joaquim River basin in Araponga, MG - Brazil. In the methodology were performed field expeditions to collect the training samples and validation using GPS. In the laboratory, supervised classifications were performed on IKONOS image by the algorithms of Maximum Likelihood and Artificial Neural Networks (Backpropagation Error) and regions (Bhattacharya). Among the classifiers evaluated in this experiment, the classification by regions had the best result, with Kappa of 0.76. The ratings by Maximum Likelihood and Artificial Neural Networks were respectively 0.48 and Kappa 0.51. This demonstrates the great potential that the supervised classification by segmentation have on classifying areas with many classes of land cover and soil and heterogeneous intra-class patterns. Thus, the findings of this study besides being useful for future planning in the watershed, will serve as universal knowledge to use classification and land cover in other areas with agro-ecological management.
%9 Processamento de Imagens
%@language pt
%3 p1357.pdf