Abstract
In order to differentiate classes of oil-spills on water surface, a neural network (NN) approach is applied for spectral data analysis and identification of airborne laser fluorosensor in this paper. The target to be detected may be one of the following: seawater, lube, diesel, etc. The primary requirement for airborne sensors is to identify the substances targeted by the laser beam. Pearson Correlation Coefficient (PCC) method is one of the most current approaches. This paper outlines the NN model for the identification of the spilled oils, and makes a comparison with PCC in an effort to increase the level of confidence in the identification results. The results of ground tests using known targets show an increased confidence in the results when using the NN Model compared to that of PCC. It is believed that the NN model would play a significant role in the ocean oil-spill identification in the future.
Funded by the National Natural Science Foundation of China. (NSFC Project 40346028)
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References
Wang, N.L., Pan, X.G.: Application of Matlab/NNTool in Neural Network System. Computer Simulation 4, 125–128 (2004)
Lin, B., An, J.B.: Study on Detection Method of Spilled Oil at Sea by ANN of Laser Remote Sensing. Marine Environmental Science 1, 47–49 (2004)
Keiner, E.L.: Estimating Oceanic Chlorophyll Concentrations with Neural Networks. Remote Sensing 1, 189–194 (1999)
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© 2004 Springer-Verlag Berlin Heidelberg
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Lin, B., An, J., Carl, B., Zhang, H. (2004). Neural Networks in Detection and Identification of Littoral Oil Pollution by Remote Sensing . In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_161
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DOI: https://doi.org/10.1007/978-3-540-28647-9_161
Publisher Name: Springer, Berlin, Heidelberg
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