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Proceedings Paper

Neural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensing
Author(s): Bin Lin; Jubai An; Carl E. Brown; Weiwei Chen
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Paper Abstract

In this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.

Paper Details

Date Published: 8 May 2003
PDF: 11 pages
Proc. SPIE 4892, Ocean Remote Sensing and Applications, (8 May 2003); doi: 10.1117/12.466789
Show Author Affiliations
Bin Lin, Dalian Maritime Univ. (China)
Jubai An, Dalian Maritime Univ. (China)
Carl E. Brown, Canada Environment Technology Ctr. (Canada)
Weiwei Chen, Dalian Maritime Univ. (China)


Published in SPIE Proceedings Vol. 4892:
Ocean Remote Sensing and Applications
Robert J. Frouin; Yeli Yuan; Hiroshi Kawamura, Editor(s)

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