Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images

Djelloul Mokadem , Abdelmalek Amine , Zakaria Elberrichi , David Helbert (1, 2)

1 UP - Université de Poitiers = University of Poitiers
2 XLIM-ASALI - Synthèse et analyse d'images

In this article, the detection of urban areas on satellite multispectral Landsat images. The goal is to improve the visual interpretations of images from remote sensing experts who often remain subjective. Interpretations depend deeply on the quality of segmentation which itself depends on the quality of samples. A remote sensing expert must actually prepare these samples. To enhance the segmentation process, this article proposes to use genetic algorithms to evolve the initial population of samples picked manually and get the most optimal samples. These samples will be used to train the Kohonen maps for further classification of a multispectral satellite image. Results are obtained by injecting genetic algorithms in sampling phase and this paper proves the effectiveness of the proposed approach

Mots clés: Genetic Algorithms; Kohonen Maps; Multispectral Satellite Image; Segmentation; Urban Areas

Domaines: Traitement du signal et de l'image [eess.SP]

Djelloul Mokadem, Abdelmalek Amine, Zakaria Elberrichi, David Helbert. Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images. International Journal of Organizational and Collective Intelligence (IJOCI), 2018, 8 (1), pp.46 - 62. ⟨10.4018/IJOCI.2018010104⟩. ⟨hal-01696710⟩