Date of Award


Level of Access Assigned by Author

Campus-Only Thesis

Degree Name

Master of Science (MS)


Electrical and Computer Engineering


Habtom W. Ressom

Second Committee Member

Mohamad T. Musavi

Third Committee Member

Richard Eason

Additional Committee Members

Bruce Segee


A system incorporating a fuzzy c-means clustering and an ensemble of artificial neural networks (ANNs) is proposed to estimate chlorophyll-a (Chl a) concentration from remotely sensed reflectance (Rrs) measurements. Fuzzy c-means is used to measure and define multiple spectral clusters from a pre-specified training set. A radial basis function (RBF) neural network is used to emulate the function of the fuzzy c-means clustering to determine the cluster and grade of membership for previously unseen spectral measurements. Next, a feed forward multi-layer perceptron (MLP) neural network is incorporated and used for Chl a estimation. The proposed method can be used to estimate Chl a concentration from Rrs measured at various global oceanic locations representing heterogeneous water types. The performance of the proposed method is presented in two experiments representing a proof of concept and a potential global Chl a prediction model. The two experiments are compared to the traditional approach, where a single ANN is used for all water types. It is shown that the cluster-based approach has the potential to build a more global Chl a prediction model.