2,518 research outputs found
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
Belief Approach for Social Networks
Nowadays, social networks became essential in information exchange between
individuals. Indeed, as users of these networks, we can send messages to other
people according to the links connecting us. Moreover, given the large volume
of exchanged messages, detecting the true nature of the received message
becomes a challenge. For this purpose, it is interesting to consider this new
tendency with reasoning under uncertainty by using the theory of belief
functions. In this paper, we tried to model a social network as being a network
of fusion of information and determine the true nature of the received message
in a well-defined node by proposing a new model: the belief social network
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