Candidate Oil Spill Detection in SLAR Data - A Recurrent Neural Network-based Approach

@inproceedings{OvidiuOprea2017CandidateOS,
  title={Candidate Oil Spill Detection in SLAR Data - A Recurrent Neural Network-based Approach},
  author={Sergiu Ovidiu-Oprea and Pablo Gil and Damian Mira and Beatriz Alacid},
  booktitle={ICPRAM},
  year={2017}
}
  • Sergiu Ovidiu-Oprea, Pablo Gil, +1 author Beatriz Alacid
  • Published in ICPRAM 2017
  • Computer Science
  • This work was supported by the Spanish Ministry of Economy and Competitiveness through the research project ONTIME (RTC-2014-1863-8). 

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Citations

    Publications citing this paper.
    SHOWING 1-4 OF 4 CITATIONS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 28 REFERENCES

    Keras

    • F. Chollet
    • GitHub repository: https://github. com/fchollet/keras.
    • 2015
    VIEW 2 EXCERPTS
    HIGHLY INFLUENTIAL

    Tensorflow : Large - scale machine learning on heterogeneous systems , 2015

    • M. Abadi, A. Agarwal, +7 authors M. Devin
    • 2015
    VIEW 1 EXCERPT
    HIGHLY INFLUENTIAL

    SMOTE: Synthetic Minority Over-sampling Technique

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Bidirectional recurrent neural networks

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Deep Learning

    VIEW 2 EXCERPTS

    Deep learn

    • Y. LeCun, Y. Bengio, G. Hinton
    • Nature
    • 2015
    VIEW 1 EXCERPT

    Deep learning

    • Y. LeCun, Y. Bengio, G. Hinton
    • Nature, 521(7553):436–444.
    • 2015
    VIEW 2 EXCERPTS

    Adam: A Method for Stochastic Optimization

    VIEW 2 EXCERPTS