Open AccessDetection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images
Author(s) -
Djelloul Mokadem,
Abdelmalek Amine,
Zakaria Elberrichi,
David Helbert
Publication year - 2018
Publication title -
international journal of organizational and collective intelligence
Language(s) - English
Resource type - Journals
eISSN - 1947-9352
pISSN - 1947-9344
DOI - 10.4018/ijoci.2018010104
Subject(s) - multispectral image , self organizing map , segmentation , computer science , genetic algorithm , multispectral pattern recognition , remote sensing , artificial intelligence , population , sampling (signal processing) , process (computing) , satellite , pattern recognition (psychology) , image segmentation , data mining , computer vision , geography , machine learning , artificial neural network , engineering , demography , filter (signal processing) , sociology , aerospace engineering , operating system
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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