Estimation of Ocean Water Chlorophyll-A Concentration Using Fuzzy C-Means Clustering and Artificial Neural Networks
Date of Award
Level of Access Assigned by Author
Master of Science (MS)
Electrical and Computer Engineering
Habtom W. Ressom
Second Committee Member
Mohamad T. Musavi
Third Committee Member
Additional Committee Members
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.
Turner, Kevin Michael, "Estimation of Ocean Water Chlorophyll-A Concentration Using Fuzzy C-Means Clustering and Artificial Neural Networks" (2007). Electronic Theses and Dissertations. 2579.