Automatic red tide detection from MODIS satellite images

Authors:
Weijian Cheng;Lawrence O. Hall;Dmitry B. Goldgof;Inia M. Soto;Chuanmin Hu
Affiliations:
Department of Computer Science and Engineering, University of South Florida, Tampa;Department of Computer Science and Engineering, University of South Florida, Tampa;Department of Computer Science and Engineering, University of South Florida, Tampa;College of Marine Science, University of South Florida, St. Petersburg;College of Marine Science, University of South Florida, St. Petersburg
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

Red tides pose a significant environmental and economic threat in the Gulf of Mexico. Timely detection of red tides is important for understanding this phenomenon. In this paper, learning approaches based on k-nearest neighbors, random forests and support vector machines have been evaluated for red tide detection from MODIS satellite images. Detection results from our algorithms were compared with ground truth red tide data collected in situ. Our results show that red tide identification methods based on machine learning approaches outperform baseline algorithms based on bio-optical characterization.