%0 Journal Article
%4 dpi.inpe.br/plutao/2012/11.28.19.14.50
%2 dpi.inpe.br/plutao/2012/11.28.19.14.51
%@doi 10.1007/978-3-642-33275-3_98
%@issn 0302-9743
%F lattes: 9840759640842299 2 NegriDutrSant:2012:StApMi
%T Stochastic Approaches of Minimum Distance Method for Region Based Classification
%D 2012
%A Negri, Rogerio Galanti,
%A Dutra, Luciano Vieira,
%A Sant'Anna, Sidnei JoÃo Siqueira,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress
%@electronicmailaddress dutra@dpi.inpe.br
%B Lecture Notes in Computer Science
%V 7441
%N 2012
%P 797-804
%K Classification process, Image simulations, Minimum average distance, Minimum distance, Region-based, Remote sensing image classification, Second variation, Simple approach, Simulation studies, Stochastic approach, stochastic distances, Imagens de Sensoriamento Remoto, Reconhecimento de Padroes, Segmentação de imagens.
%X Normally remote sensing image classification is performed pixelwise which produces a noisy classification. One way of improving such results is dividing the classification process in two steps. First, uniform regions by some criterion are detected and afterwards each unlabeled region is assigned to class of the "nearest" class using a so-called stochastic distance. The statistics are estimated by taking in account all the reference pixels. Three variations are investigated. The first variation is to assign to the unlabeled region a class that has the minimum average distance between this region and each one of reference samples of that class. The second is to assign the class of the closest reference sample. The third is to assign the most frequent class of the k closest reference regions. A simulation study is done to assess the performances. The simulations suggested that the most robust and simple approach is the second variation.
%@language en
%3 Paper-PublishedVersion-74410797.pdf
%O 17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012
%O Buenos Aires
%O 3 September 2012through6 September 2012
%O Code92323