Discovering Rules with Genetic Algorithms to Classify Urban Remotely Sensed Data
BibTeX
@MISC{D_discoveringrules,
author = {Sheeren D and Quirin A and Puissant A and Gançarski P and Weber C and Sheeren D and Quirin A and Gançarski P and Puissant A and Weber C},
title = {Discovering Rules with Genetic Algorithms to Classify Urban Remotely Sensed Data},
year = {}
}
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Abstract
Abstract — The classification methods applied in the objectoriented image analysis approach are often based on the use of domain knowledge. A key issue in this approach is the acquisition of this knowledge which is generally implicit and not formalized. In this paper, we examine the possibilities of using genetic programming for the automatic extraction of classification rules from urban remotely sensed data. The method proposed is composed of several steps: segmentation, feature extraction, selection of training sets, acquisition of rules, classification. Features related to the spectral, spatial and contextual properties of the objects are used in the classification procedure. Experiments are made on a Quickbird MS image. The quality of the results shows the effectiveness of the proposed genetic classifier in the object-oriented, knowledge-based approach. Keywords- genetic classifier, knowledge-based classification method, object-oriented approach. I.
