Title: Hyperspectral image analysis for oil spill detection: a comparative study

Authors: Sahar A. El-Rahman; Ali Hussein Saleh Zolait

Addresses: Faculty of Engineering-Shoubra, Benha University, Cairo, Egypt; College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia ' Department of Information Systems, College of Information Technology, University of Bahrain, Sakhir, Kingdom of Bahrain

Abstract: In the last years, oil spill detection by hyperspectral imaging has been transferred from experimental to operational. In this paper, researchers attempted to use and compare four classification approaches for the identification of oil spills. The hyperspectral image classification approaches 'namely' are support vector machine (SVM), parallelepiped, minimum distance (MD) and binary encoding (BE). These approaches used to identify the oil spill areas in both two study areas which are selected as oil-spill areas in the Gulf of Mexico and the Adriatic Sea. The classifiers are applied to the study areas after pre-processing that include the spatial and spectral subset and atmospheric correction. Whereas, the classifiers applied to the full dataset and region of interest (ROI) before and after performing principal component analysis (PCA). The PCA is utilised to eliminate redundant data, reduce the vast amount of information and consequently, decrease the processing times. The findings indicate that the SVM, MD and BE approaches supply a high classification accuracy better than parallelepiped approach using both datasets obtained from both selected region.

Keywords: binary encoding approach; hyperspectral image; minimum distance approach; parallelepiped approach; principal component analysis; PCA; support vector machine; SVM.

DOI: 10.1504/IJCSM.2018.091744

International Journal of Computing Science and Mathematics, 2018 Vol.9 No.2, pp.103 - 121

Accepted: 06 Oct 2017
Published online: 14 May 2018 *

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