Modeling of Suspended Particulate Matter in the Algerian Coast Using Neural Networks and Mathematical Morphology

In this paper, we propose a methodology for the characterization of the suspended particulate matter along the Algiers’s bay. An approach by multi-layer perceptron (MLP) with training by back propagation of the gradient optimized by the algorithm of Levenberg-Marquardt (LM) is used. The accent was put on the choice of the components of the base of training where a comparative study made for four methods: Random and three alternatives of classification by K-Means. The samples are taken from suspended matter image, obtained by an analytical model based on polynomial regression by taking account of in situ measurements. The mask which selects the region of interest (water in our case) was done by using a multi spectral classification with ISODATA algorithm. To improve the result of classification, a cleaning of this mask was carried out using the mathematical morphology tools.