%0 Conference Proceedings
%4 sid.inpe.br/mtc-m16c/2017/12.01.20.06
%2 sid.inpe.br/mtc-m16c/2017/12.01.20.06.45
%@issn 2179-4820
%T Remote sensing image information mining applied to burnt forest detection in the brazilian Amazon
%D 2017
%A Pletsch, Mikhaela A. J. S.,
%A Korting, Thales Sehn,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%E Davis Jr., Clodoveu A. (UFMG),
%E Queiroz, Gilberto R. de (INPE),
%B Simpósio Brasileiro de Geoinformática, 18 (GEOINFO)
%C Salvador
%8 04-06 dez. 2017
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 322-333
%S Anais
%X Fire processes contribute to carbon dioxide emissions, main gas re- sponsible for the Greenhouse Effect. Considering the importance of fire pro- cesses management for the detection of burnt areas in the Brazilian Amazon, the Linear Spectral Mixture Model is one of the main methods available. Nonethe- less, some manual processes are required before its application, such as iden- tifying adequate images in databases. In this manner, we have developed an approach for Remote Sensing Image Information Mining (ReSIIM), which was tested for burnt areas studies. ReSIIM stores information about well-known tar- gets found in Remote Sensing imagery, such as cloud, cloud shadow, clear land, water, vegetation and bare soil.
%@language pt
%3 40pletsch_korting.pdf