Toward A Modular Connectionist Model of Local Chlorophyll Concentration from Satellite Images

Monitoring the values of physical variables in water at ground level (in a river, lake or ocean) on the basis of noisy images acquired from a geostationary satellite is a relevant and challenging task. This paper introduces a 3-module neural system that allows for automatic monitoring of the local concentration of chlorophyll in presence of clouds and turbid water at specific locations. The system relies on images in four different wavelength intervals taken by the satellite over specific points of the lake surface. Module (1) estimates the probability of presence of clouds over dots in the image, possibly applying the reject option, and feeding this information into the following nets. Module (2) estimates. the turbidity of water, i.e., its transparency, on a dot-by-dot basis. Finally, module (3) is a neural network with adaptive amplitude of activation functions, featuring a combination of linear and nonlinear terms, that realizes a regression model to describe the relationship between its inputs (i.e., a 4-bands dot in the satellite image plus the corresponding outputs from the previous two modules) and the desired concentration of chlorophyll in the corresponding location of the lake. Eventually, a global tuning of the whole system parameters is possible. Experiments involving noisy data, sampled from the water of Lake Montepulciano in Tuscany (Italy), are presented. The problem of the limited availability of "target" training data, i.e. physical measurements obtained by sampling from the lake surface on boats, is addressed. Results are compared with standard multivariate linear regression models.