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Proceedings Paper

Application of genetic programming and Landsat multi-date imagery for urban growth monitoring
Author(s): Khelifa Djerriri; Mimoun Malki
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Paper Abstract

Monitoring of earth surface changes from space by using multi-date satellite imagery was always a main concern to researchers in the field of remotely sensed image processing. Thus, several techniques have been proposed to saving technicians from interpreting and digitizing hundreds of areas by hand. The exploiting of simple, easy to memorize and often comprehensible mathematical models such band-ratios and indices are one of the widely used techniques in remote sensing for the extraction of particular land-cover/land-use like urban and vegetation areas. The results of these models generally only need the definition of adequate threshold or using simple unsupervised classification algorithms to discriminate between the class of interest and the background. In our work a genetic programming based approach has been adopted to evolve simple mathematical expression to extract urban areas from image series. The model is built from a single image by using a basic set of operators between spectral bands and maximizing a fitness function, which is based on the using of the M-statistic criterion. The model was constructed from the Landsat 5 TM image acquired in 2006 by using training samples extracted with the help of a Quick-bird high spatial resolution satellite image acquired the same day as the Landsat image over the city of Oran, Algeria. The model has been tested to extract urban areas from multi-date series of Landsat TM imagery

Paper Details

Date Published: 17 October 2013
PDF: 9 pages
Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 889210 (17 October 2013); doi: 10.1117/12.2029309
Show Author Affiliations
Khelifa Djerriri, Ctr. National des Techniques Spatiales (Algeria)
Univ. de Sidi-Bel-Abbes (Algeria)
Mimoun Malki, Univ. de Sidi-Bel-Abbes (Algeria)

Published in SPIE Proceedings Vol. 8892:
Image and Signal Processing for Remote Sensing XIX
Lorenzo Bruzzone, Editor(s)

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