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The application of uncertainty measures in the training and evaluation of supervised classifiers
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Orientador(es)
Resumo(s)
The production of thematic maps from remotely sensed images requires the application
of classification methods. A great variety of classifiers are available, producing
frequently considerably different results. Therefore, the automatic extraction of
thematic information requires the choice of the most appropriate classifier for each
application. One of the main objectives of the research described in this article is to
evaluate the performance of supervised classifiers using the information provided
by the application of uncertainty measures to the testing sets, instead of statistical
accuracy indices. The second main objective is to show that the information provided
by the uncertainty measures for the training set may be used to assess and
redefine the sample sites included in this set, in order to improve the classification
results. To achieve the proposed objectives, two supervised classifiers, one probabilistic
and another fuzzy, were applied to a very high spatial resolution (VHSR)
image. The results show that similar conclusions on the classifiers’ performance
are obtained with the uncertainty measures and the traditional accuracy indices
obtained from error matrices. It is also shown that the redefinition of the training
set based on the information provided by the uncertainty measures may generate
more accurate outputs.
Descrição
Palavras-chave
Remote sensing Uncertainty measures Fuzzy classifiers
Contexto Educativo
Citação
Editora
Taylor & Francis