INESCC-DL - Artigos em Livros de Actas
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Browsing INESCC-DL - Artigos em Livros de Actas by Field of Science and Technology (FOS) "Ciências Naturais::Ciências da Computação e da Informação"
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- An Evolutionary Algorithm based on an outranking relation for sorting problemsPublication . Oliveira, Eunice; Antunes, Carlos HenggelerA new approach for using the preferences elicited from a Decision Maker (DM) into the operational framework of an Evolutionary Algorithm (EA) is presented. The preference representation is achieved using the parameters and principles of the ELECTRE TRI method devoted to the sorting problem. The outranking relation is used to replace the non-dominance relation in the usual operators in the EA (crossover, mutation and selection operator). The aim of this approach is to focus the search on the region of interest defined by the DM's preferences and consequently restrict the number of solutions in the Pareto-optimal front to be subject to further screening. This aspect is particularly important when dealing with problems that lead to a large number of non-dominated solutions.
- On the information provided by uncertainty measures in the classification of remote sensing imagesPublication . Gonçalves, Luisa; Fonte, Cidália C.; Júlio, Eduardo N.B.S.; Caetano, MarioThis paper investigates the potential information provided to the user by the uncertainty measures applied to the possibility distributions associated with the spatial units of an IKONOS satellite image, generated by two fuzzy classifiers, based, respectively, on the Nearest Neighbour Classifier and the Minimum Distance to Means Classifier. The deviation of the geographic unit characteristics from the prototype of the class to which the geographic unit is assigned is evaluated with the Un non-specificity uncertainty measures proposed by [1] and the exaggeration uncertainty measure proposed by [2]. The classifications were evaluated using accuracy and uncertainty indexes to determine their compatibility. Both classifications generated medium to high levels of uncertainty for almost all classes, and the global accuracy indexes computed were 70% for the Nearest Neighbour Classifier and 53% for the Minimum Distance to Means Classifier. The results show that similar conclusions can be obtained with accuracy and uncertainty indexes and the latter, along with the analysis of the possibility distributions, may be used as indicators of the classification performance and may therefore be very useful tools. Since the uncertainty indexes may be computed to all spatial units, the spatial distribution of the uncertainty was also analysed. It's visualization shows that regions where less reliability is expected present a great amount of detail that may be potentially useful to the user.
- Using Uncertainty Information to Combine Soft ClassificationsPublication . Gonçalves, Luisa M. S.; Fonte, Cidália C.; Caetano, MarioThe classification of remote sensing images performed with different classifiers usually produces different results. The aim of this paper is to investigate whether the outputs of different soft classifications may be combined to increase the classification accuracy, using the uncertainty information to choose the best class to assign to each pixel. If there is disagreement between the outputs obtained with the several classifiers, the proposed method selects the class to assign to the pixel choosing the one that presents less uncertainty. The proposed approach was applied to an IKONOS image, which was classified using two supervised soft classifiers, the Multi-layer Perceptron neural network classifier and a fuzzy classifier based on the underlying logic of the Minimum-Distance-to-Means. The overall accuracy of the classification obtained with the combination of both classifications with the proposed methodology was higher than the overall accuracy of the original classifications, which shows that the methodology is promising and may be used to increase classification accuracy.