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Detection of Urban Areas Using Genetic Algorithms and Kohonen Maps on Multispectral Images
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Author(s): Djelloul Mokadem (GeCoDe Laboratory, Tahar Moulay University of Saida, Algeria), Abdelmalek Amine (GeCoDe Laboratory, Department of Computer Sciences, Dr. Tahar Moulay University of Saida, Algeria), Zakaria Elberrichi (Djllali Liabes University of SidiBelAbbes, Algeria)and David Helbert (University of Poitiers, France)
Copyright: 2019
Pages: 19
Source title:
Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8054-6.ch033
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Abstract
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.
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