Fechar
Metadados

%0 Conference Proceedings
%4 sid.inpe.br/mtc-m16c/2020/12.14.12.05
%2 sid.inpe.br/mtc-m16c/2020/12.14.12.05.01
%@issn 2179-4847
%T Circular Hough Transform and Balanced Random Forest to Detect Center Pivots
%D 2020
%A Rodrigues, Marcos Lima,
%A Körting, Thales Sehn,
%A Queiroz, Gilberto Ribeiro de,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress marcos.rodrigues@inpe.br
%@electronicmailaddress thales.korting@inpe.br
%@electronicmailaddress gilberto.queiroz@inpe.br
%E Carneiro, Tiago Garcia de Senna (UFOP),
%E Felgueiras, Carlos Alberto (INPE),
%B Simpósio Brasileiro de Geoinformática, 21 (GEOINFO)
%C On-line
%8 30 nov. a 03 dez. 2020
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 106-115
%S Anais
%X Water management is a field related to the increased mechanization of agriculture, mainly through center pivot irrigation systems, therefore it is important to identify and quantify these systems. Currently, with 6.95 million hectares, Brazil is among the 10 largest countries in irrigation areas in the world. In this study, a combined Computer Vision and Machine Learning ap- proach is proposed for the identification of center pivots in remote sensing im- ages. The methodology is based on Circular Hough Transform (CHT) to target detection and Balanced Random Forest (BRF) classifier using vegetation indices NDVI and SAVI generated from Landsat 8 and CBERS 4 images, being able to detect up to 90.48% of center pivots mapped by the Brazilian National Water Agency (ANA).
%9 Geoinformação
%@language en
%3 p10.pdf


Fechar