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

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    Type
    Article
    Authors
    Hittawe, Mohamad
    Afzal, Shehzad
    Jamil, Tahira
    Snoussi, Hichem
    Hoteit, Ibrahim cc
    Knio, Omar cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    King Abdullah Univ. of Science and Technology
    Physical Science and Engineering (PSE) Division
    Date
    2019-03-13
    Permanent link to this record
    http://hdl.handle.net/10754/652843
    
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    Abstract
    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.
    Citation
    Hittawe 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.
    Publisher
    SPIE-Intl Soc Optical Eng
    Journal
    Journal of Electronic Imaging
    DOI
    10.1117/1.JEI.28.2.021012
    Additional Links
    https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-28/issue-02/021012/Abnormal-events-detection-using-deep-neural-networks--application-to/10.1117/1.JEI.28.2.021012.full
    ae974a485f413a2113503eed53cd6c53
    10.1117/1.JEI.28.2.021012
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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