Vol. 6, No. 2, 115-129, 2007

Classification with Artificial Neural Networks and Support Vector Machines: Application to oil fluorescence spectra
Khaled Mohamed Almhdi, Paolo Valigi, Vidas Gulbinas, Rainer Westphal and Rainer Reuter

Abstract
This paper reports on oil classification by fluorescence spectroscopy. The investigations are part of the development of a laser-based remote sensor (laser fluorosensor) to be used for the detection and classification of oil spills on water surfaces. The polychromator of the fluorosensor has six channels for measuring signals that represent the spectral fluorescence signature of the detected oil in the UV/VIS wavelength range following excitation at 355 nm wavelength. The investigation of the oil classification is based on the shape of the signature of the oil detected by these channels. The investigation uses three methods to examine crude oils, heavy refined oils, and sludge oils: the channels relationships method (CRM); artificial neural networks (ANNs); and support vector machines (SVMs). This was done on a laboratory database of oil fluorescence spectra.

The database and the input fluorescence signature of the oils play a very important role in the efficiency of the classification method. If the input fluorescence of the oil does not fit into one of the classes already included in the database or if it is a completely new and previously not considered signature, then the training process for classification must always be redone. Generally, all three methods yield promising results and can be used for the detection and classification of oil spills on water surfaces. The channelsÂ’ relationship method provides a meaningful classification of the available spectra, according to a rough oil type estimation. More specific substance information can be achieved with ANNs and SVMs. Both SVMs and ANNs prove to be efficient, fast, and reliable and have real-time capabilities. The SVM method is faster and more stable than ANN. Therefore, it is considered to be the most convenient method for classifying spectral information

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History
Submitted: 26 July 2006
Revised: 31 Aug 2007
Accepted: 12 Nov 2007
Published: 10 Dec 2007
Responsible editor: Robin Vaughan

Citation
Almhdi K M, P Valigi, V Gulbinas, R Westphal & R Reuter, 2007. Classification with Artificial Neural Networks and Support Vector Machines: Application to oil fluorescence spectra. EARSeL eProceedings, 6(2): 115-129

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EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France

   
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BIS Library and Information System, Carl von Ossietzky University of Oldenburg

 

ISSN 1729-3782