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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi@80/2007/09.18.20.44
%2 sid.inpe.br/sibgrapi@80/2007/09.18.20.44.30
%A Freitas, Greice Martins de,
%A Avila, Ana Maria Heuminski de,
%A Papa, Joao Paulo,
%@affiliation CEPAGRI/UNICAMP
%@affiliation CEPAGRI/UNICAMP
%@affiliation IC/UNICAMP
%T Semi-Supervised Support Vector Rainfall Estimation Using Satellite Images
%B Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
%D 2007
%E Gonçalves, Luiz,
%E Wu, Shin Ting,
%S Proceedings
%8 Oct. 7-10, 2007
%J Porto Alegre
%I Sociedade Brasileira de Computação
%C Belo Horizonte
%K rainfall estimation, semi-supervised support vector machines.
%X In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (S3VM). The S3VM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite estimation, due to the large amount of unlabeled data. .
%@language en
%3 freitas.pdf


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