Title :
Automatic red tide detection from MODIS satellite images
Author :
Cheng, Weijian ; Hall, Lawrence O. ; Goldgof, Dmitry B. ; Soto, Inia M. ; Hu, Chuanmin
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA
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.
Keywords :
geophysics computing; learning (artificial intelligence); oceanographic techniques; remote sensing; support vector machines; Florida; Gulf of Mexico; MODIS satellite images; United States; ground truth data; k-nearest neighbors; machine learning approaches; random forests; red tide detection; red tide identification methods; support vector machines; Clustering algorithms; MODIS; Machine learning; Machine learning algorithms; Oceans; Partitioning algorithms; Satellites; Support vector machines; Tides; USA Councils; Florida´s red tides; k-nearest neighbors; random forests; remote sensing; support vector machines;
Conference_Titel :
Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-2793-2
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2009.5346189