Abnormal events detection using deep neural networks: Application to extreme sea surface temperature detection in the Red Sea

Published

Journal Article

© 2019 SPIE and IS & T. We 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.

Full Text

Duke Authors

Cited Authors

  • Hittawe, MM; Afzal, S; Jamil, T; Snoussi, H; Hoteit, I; Knio, O

Published Date

  • March 1, 2019

Published In

Electronic International Standard Serial Number (EISSN)

  • 1560-229X

International Standard Serial Number (ISSN)

  • 1017-9909

Digital Object Identifier (DOI)

  • 10.1117/1.JEI.28.2.021012

Citation Source

  • Scopus