What is it about?
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.
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Why is it important?
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 …
Perspectives
The hyper-spectral 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.
Associate Professor Dr. Ali Hussein Zolait
University of Bahrain
Read the Original
This page is a summary of: Hyperspectral image analysis for oil spill detection: a comparative study, International Journal of Computing Science and Mathematics, January 2018, Inderscience Publishers,
DOI: 10.1504/ijcsm.2018.10012786.
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