Uncertainty analysis in the representation of the urban land cover classes through the application of artificial neural network

Abstract

The great diversity of materials that characterizes the urban environment determines
a structure of mixed classes in a classification of multiespectral images. In that
sense, it is important to define an appropriate classification system using a non
parametric classifier, that allows incorporating non spectral (such as texture) data to
the process. They also allow analyzing the uncertainty associated to each class from
the output values of the network calculated in relation to each class. Considering
these properties, an experiment was carried out. This experiment consisted in the
application of an Artificial Neural Network aiming at the classification of the urban
land cover of Presidente Prudente and the analysis of the uncertainty in the
representation of the mapped thematic classes. The results showed that it is possible
to discriminate the variations in the urban land cover through the application of an
Artificial Neural Network. It was also possible to visualize the spatial variation of
the uncertainty in the attribution of classes of urban land cover from the generated
representations. The class characterized by a defined pattern as intermediary related
to the impermeability of the urban soil presented larger ambiguity degree and,
therefore, larger mixture.
Keywords: Classification of urban environment, Artificial Neural Networks,
Uncertainty in the classification, Remote Sensing.