%0 Conference Proceedings
%4 sid.inpe.br/mtc-m16d/2019/11.27.13.29
%2 sid.inpe.br/mtc-m16d/2019/11.27.13.29.22
%@issn 2179-4847
%T Land cover classifications of clear-cut deforestation using deep learning
%D 2019
%A Sanchez, Alber,
%A Picoli, Michelle,
%A Andrade, Pedro Ribeiro de,
%A Simões, Rolf,
%A Santos, Lorena,
%A Chaves, Michel,
%A Begotti, Rodrigo,
%A Camara, Gilberto,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress alber.ipia@inpe.br
%@electronicmailaddress michelle.picoli@inpe.br
%@electronicmailaddress pedro.andrade@inpe.br
%@electronicmailaddress rolf.simoes@inpe.br
%@electronicmailaddress lorena.santos@inpe.br
%@electronicmailaddress michel.chaves@inpe.br
%@electronicmailaddress rodrigo.begotti@inpe.br
%@electronicmailaddress gilberto.camara@inpe.br
%E Lisboa Filho, Jugurta,
%E Monteiro, Antonio Miguel Vieira,
%B Simpósio Brasileiro de Geoinformática, 20 (GEOINFO)
%C São José dos Campos
%8 11 -13 nov. 2019
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%S Anais do 20º Simpósio Brasileiro de Geoinformática
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%K geoinformatica.
%X Using Deep Learning Neural Networks, we made supervised classifications of a small region of the Brazilian Amazon in order to map clearcut deforestation. We organized Landsat 8 Surface Reflectance images into time series and we classify the images using the bands ad a Linear Mixture Model. We obtained similar accuracies using both data sets when compared to the data reported by the Brazilian Amazon Deforestation Monitoring Program (PRODES). These results suggest the possibilities of using automatic supervised techniques to extend the coverage of forest monitoring programs to those excluded areas by lack of human resources.
%@language pt
%3 48-56.pdf