%0 Conference Proceedings
%4 dpi.inpe.br/plutao/2013/05.31.19.29.21
%2 dpi.inpe.br/plutao/2013/05.31.19.29.22
%@isbn 978-1-4799-0211-8
%F lattes: 7413318739396387 1 BastosFonPinKorSan:2013:CoAn
%T Intraurban land cover classification using IKONOS II images and data mining techniques: a comparative analysis
%D 2013
%A Bastos, Vanessa da Silva Brum,
%A Fonseca, Leila Maria Garcia,
%A Pinho, Carolina Moutinho Duque de,
%A Korting, Thales Sehn,
%A Santos, Rafael Duarte Coelho dos,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Centro de política e economia do setor público (CEPESP) Fundação Getúlio Vargas (FGV-SP) São Paulo, Brazil
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress vsbrumb@gmail.com
%@electronicmailaddress leila@dpi.inpe.br
%@electronicmailaddress
%@electronicmailaddress thales@dpi.inpe.br
%@electronicmailaddress rafael.santos@inpe.br
%B Joint Urban Remote Sensing Event, (JURSE).
%C São Paulo, SP
%8 2013
%I Institute of Electrical and Electronics Engineers
%S Anais
%K intraurban, land cover, data mining.
%X High spatial resolution image analysis acquired over urban areas has been performed with success using Geographic Object Based Analysis (GEOBIA). However, it was observed that the use of data mining techniques in the image analysis procedures can speed up the processing time by selecting the most appropriate parameters for classification process without decreasing the classification accuracy. Therefore, this work aims at comparing some algorithms for classifying intra-urban land cover using IKONOS II images and data mining techniques. Three classification algorithms, KNN, MLP and C4.5 were analyzed.
%@language en
%3 brumvanessa.pdf