The Study Of Intelligent Identification Algorithm For Detecting Littoral Oil-Spills By Laser Remote Sensing

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 fiuorosensor 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. SOM NN is the most effective and advanced one as classifier for littoral oil-spill in that SOM algorithm can extract the internal features of parameter by self-organizing. This paper has not only developed ANN models in theory but also completed software package for spectra intelligent analysis for the airborne detection of oil spills by laser-induced fluorescence.The ANN model would play a significant role in the ocean oil-spill identification in the future.