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http://dx.doi.org/10.3745/JIPS.02.0054

A Multi-Objective TRIBES/OC-SVM Approach for the Extraction of Areas of Interest from Satellite Images  

Benhabib, Wafaa (Faculty of Mathematics and Computer Science, University of Sciences and Technology of Oran Mohamed Boudiaf (USTO-MB))
Fizazi, Hadria (Faculty of Mathematics and Computer Science, University of Sciences and Technology of Oran Mohamed Boudiaf (USTO-MB))
Publication Information
Journal of Information Processing Systems / v.13, no.2, 2017 , pp. 321-339 More about this Journal
Abstract
In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.
Keywords
Image Classification; MO-TRIBES; OC-SVM; Remote Sensing;
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