Abstract
Object-based image analysis techniques give accurate results when a good knowledge base is extracted from remote sensing imagery. Data mining algorithms, especially the evolutionary process, can extract useful knowledge that can be used in different fields. In this paper, object-oriented classification was used, more particularly, the object-based image analysis approach (OBIA) is used to classify a large feature space composed of a very high spatial resolution (VHR) satellite image. The genetic programming (GP) concept was applied to extract classification rules with an induction form. This study aims to examine how data mining techniques based on the GP method can help to discover knowledge and extract classification rules automatically to illustrate well this knowledge. These rules are expected to enrich an anthology in the urban remote sensing domain. A comparison of the performance of three GP algorithms (Bojarczuk_GP, Falco_GP, and Tan_GP) was made using the JCLEC framework. Results showed two main conclusions. The first showed that generated rules can classify and extract useful knowledge from VHR satellite data using GP algorithms. The second demonstrates that the Bojarczuk model is efficient on accuracy classification than the Falco and Tan models.
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Rida, A., Hicham, A., Abderrahim, N. (2022). Optimization of Object-Based Image Analysis with Genetic Programming to Generate Explicit Knowledge from WorldView-2 Data for Urban Mapping. In: Barramou, F., El Brirchi, E.H., Mansouri, K., Dehbi, Y. (eds) Geospatial Intelligence. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-80458-9_12
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