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

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

Djelloul Mokadem (GeCoDe Laboratory, Tahar Moulay University of Saida, Algeria), Abdelmalek Amine (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saida, Algeria), Zakaria Elberrichi (Djllali Liabes University of SidiBelAbbes, Algeria) and David Helbert (Xlim, CNRS, University of Poitiers, Chasseneuil-Futuroscope, France)
DOI: 10.4018/IJOCI.2018010104

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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1. Introduction

With the recent innovations in Earth observation techniques (sensors, satellites, and data), “urban” remote sensing or “remote sensing urban applications” have rapidly gained popularity among urban planners and, more generally, administrations in charge of territorial planning.

Indeed, remote sensing makes it possible to increase our understanding of urban areas in different ways, although the real potential of this technique is often challenged by the complexity of the urban environment itself. (Xiaojun, 2011)

Previous studies have been carried out on the urban theme, notably the work of (Antoine, 2009), he tried to develop a method able to detect and qualify changes in small areas from Very High spatial Resolution (VHR) remote sensing data acquired at different dates and from different sources by comparing the textural properties of objects of interest. First, he tried to extract objects by a region growing segmentation and then, their texture are compared.

Another work has been carried out in this direction, (Ashish, 2010) used a technique based on fuzzy clustering approach which takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times.

Using always the VHR satellite data, (Tobias, 2017) proposed a novel object-based approach for unsupervised change detection with focus on individual buildings, he first applied a principal component analysis together with a unique procedure to determine the number of relevant principal components is performed as a predecessor for change detection; then he used k-means clustering for discrimination of changed and unchanged buildings.

(Andrew, 2017) used a different technique called Import Vector Machine (IVM) which builds upon the popular Support Vector Machine (SVM) methodology (Ribana, 2012). To obtain the optimum classification, the IVM algorithm explores all possible subsets of training data for optimal selection (termed import vectors) which are derived through successively adding training data samples until a given convergence criterion is met (Ribana, 2012). Data samples are selected according to their contribution to the classification solution.

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