%0 Conference Proceedings
%4 dpi.inpe.br/marte/2011/07.27.23.17
%2 dpi.inpe.br/marte/2011/07.27.23.17.20
%@isbn 978-85-17-00056-0 (Internet)
%@isbn 978-85-17-00057-7 (DVD)
%T Identificação de Embarcações em Imagens Aerotransportadas de Radar de Abertura Sintética na Área Marítima do Brasil
%D 2011
%A Gamba, Sérgio Roberto Horst,
%A Sano, Edson Eyji,
%@affiliation Comando-Geral de Operações Aéreas – COMGAR / Força Aérea Brasileira – FAB
%@affiliation Embrapa Cerrado
%@electronicmailaddress horsthess@msn.com
%@electronicmailaddress sano@cpac.embrapa.br
%E Epiphanio, José Carlos Neves,
%E Galvão, Lênio Soares,
%B Simpósio Brasileiro de Sensoriamento Remoto, 15 (SBSR).
%C Curitiba
%8 30 abr. - 5 maio 2011
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 8310-8317
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%K Remote Sensing, Radar Images, Targets. Sensoriamento Remoto, Imagens Radar, Alvos.
%X This study deals with the identification of vessels in airborne synthetic aperture radar (SAR) images. The objective is to identify the optimal GIS-based integration approaches, image enhancements, morphological filters, classifiers and processors that enable better identification of ships in SAR images from the coastal areas of Brazil. The methodology included the analysis of five digital images from three study areas (Port of Tubarão (Es), Port of Santos (SP), and Snake Island (RS)) were exported to MS Excel spreadsheet and statistical packages SPSS and MINITAB to be analyzed statistically. The images were further processed using ENVI 4.5 on different highlights (2% linear, Gaussian, equalization, square root and contrast from 50 to 200), morphological filters (dilation, erosion, opening and closing), non-supervised classifiers (ISODATA and Kmeans clustering), supervised classifiers (parallelepiped, minimum distance, Mahalanobis distance, maximum likelihood, spectral angle map, divergence of spectral information, binary encoding and support vector machine) and processors (by decorrelation highlight, saturation and synthetic color image). Results of this study showed that the the most appropriate SAR image to identify vessels was the L-band with HH, VV and VH, or HH, VV and HV polarizations, followed by application of contrast enhancement of 50-200, morphological opening filter and classifier support vector machine or synthetic color image processor.
%9 SAR: Processamento e Aplicações
%@language pt
%3 p0132.pdf