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


Wafaa Benhabib, Hadria Fizazi, Journal of Information Processing Systems Vol. 13, No. 2, pp. 321-339, Apr. 2017  

https://doi.org/10.3745/JIPS.02.0054
Keywords: Image Classification, MO-TRIBES, OC-SVM, Remote Sensing
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Abstract

In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MOTRIBES/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.


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Cite this article
[APA Style]
Benhabib, W. & Fizazi, H. (2017). A Multi-Objective TRIBES/OC-SVM Approach for the Extraction of Areas of Interest from Satellite Images . Journal of Information Processing Systems, 13(2), 321-339. DOI: 10.3745/JIPS.02.0054.

[IEEE Style]
W. Benhabib and H. Fizazi, "A Multi-Objective TRIBES/OC-SVM Approach for the Extraction of Areas of Interest from Satellite Images ," Journal of Information Processing Systems, vol. 13, no. 2, pp. 321-339, 2017. DOI: 10.3745/JIPS.02.0054.

[ACM Style]
Wafaa Benhabib and Hadria Fizazi. 2017. A Multi-Objective TRIBES/OC-SVM Approach for the Extraction of Areas of Interest from Satellite Images . Journal of Information Processing Systems, 13, 2, (2017), 321-339. DOI: 10.3745/JIPS.02.0054.