Title

A machine learning based spatio-temporal data mining approach for coastal remote sensing data

Advisor

King, L. Roger

Committee Member

Durbha, S. Surya

Committee Member

Banicescu, Ioana

Committee Member

Younan, H. Nicholas

Date of Degree

8-1-2010

Original embargo terms

MSU Only Indefinitely

Document Type

Dissertation - Open Access

Major

Computer Engineering

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Continuous monitoring of coastal ecosystems aids in better understanding of their dynamics and inherent harmful effects. As many of these ecosystems prevail over space and time, there is a need for mining this spatio-temporal information for building accurate monitoring and forecast systems. Harmful Algal Blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been devising a state-of-the-art monitoring and forecasting systems for these HAB events. The efficacy of a monitoring and forecasting system relies on the performance of HAB detection. A Machine Learning based Spatio-Temporal data mining approach for the detection of HAB (STML-HAB) events in the region of Gulf of Mexico is proposed in this work. The spatio-temporal cubical neighborhood around the training sample is considered to retrieve relevant spectral information pertaining to both HAB and Non-HAB classes. A unique relevant feature subset combination is derived through evolutionary computation technique towards better classification of HAB from Non-HAB. Kernel based feature transformation and classification is used in developing the model. STML-HAB model gave significant performance improvements over the current optical detection based techniques by highly reducing the false alarm rate with an accuracy of 0.9642 on SeaWiFS data. The developed model is used for prediction on new datasets for further spatio-temporal analyses such as the seasonal variations of HAB, and sequential occurrence of algal blooms. New variability visualizations are introduced to illustrate the dynamic behavior and seasonal variations of HABs from large spatiotemporal datasets. The results outperformed the ensemble of the currently available empirical methods for HAB detection. The ensemble method is implemented by a new approach for combining the empirical models using a probabilistic neural network model. The model is also compared with the results obtained using various feature extraction techniques, spatial neighborhoods and classifiers.

URI

https://hdl.handle.net/11668/16325

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