Abnormal events detection using deep neural networks: application to extreme sea surface temperature detection in the Red Sea
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Science and Engineering Program
King Abdullah Univ. of Science and Technology
Physical Sciences and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/652843
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AbstractWe present a method based on deep learning for detecting and localizing abnormal/extreme events in sea surface temperature (SST) of the Red Sea images using training samples of normal events only. The method operates in two stages; the first one involves features extraction from each patch of the SST input image using the first two convolutional layers extracted from a pretrained convolutional neural network. In the second stage, two methods are used for training the model from the normal training data. The first method uses one-class support vector machine (1-SVM) classifier that allows a fast and robust abnormal detection in the presence of outliers in the training dataset. In the second method, a Gaussian model is defined on the Mahalanobis distances between all normal training data. Experimental tests are conducted on satellite-derived SST data of the Red Sea spanning for a period of 31 years (1985-2015). Our results suggest that the Gaussian model of Mahalanobis distances outperformed 1-SVM by providing better performance in terms of sensitivity and specificity.
CitationHittawe MM, Afzal S, Jamil T, Snoussi H, Hoteit I, et al. (2019) Abnormal events detection using deep neural networks: application to extreme sea surface temperature detection in the Red Sea. Journal of Electronic Imaging 28: 1. Available: http://dx.doi.org/10.1117/1.JEI.28.2.021012.
PublisherSPIE-Intl Soc Optical Eng
JournalJournal of Electronic Imaging