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International Symposium on Ambient Intelligence

ISAmI 2022: Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence pp 54–64Cite as

Automatic Detection of Oil Spills from SAR Images Using Deep Learning

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 603))

Abstract

Large tankers, ships, and pipeline cracks pour oil on sea surfaces, causing significant damage and devastation to the maritime ecosystem. From the synthetic aperture radar (SAR) images, the target scenarios are ships, sea, land surfaces, look-alikes, and oil spills. Oil spill detection and segmentation using SAR pictures is critical for leak cleaning and environmental protection. The paper presents a deep learning-based framework for the oil spill identification on the dataset generated by the European Maritime Safety Agency (EMSA). In the first step, a 23-layer Convolutional Neural Network detects oil spills by classifying the patches into less than 0.5% and more than 0.5% oil spill pixels. The second step uses a U-Net architecture to apply the semantic segmentation on the generated patches. The results evidenced that the deep learning framework and segmentation models outperform the identification of an oil spill from SAR images. The present research makes a substantial contribution to future research on oil spill detection and the processing of SAR images.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Charotar University of Science And Technology (CHARUSAT), Charusat Campus, Changa, 388421, India

    Krishna Patel

  2. Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, India

    Chintan Bhatt

  3. BISITE Research Group, University of Salamanca, 37007, Salamanca, Spain

    Juan M. Corchado

  4. Air Institute, IoT Digital Innovation Hub, 37188, Salamanca, Spain

    Juan M. Corchado

  5. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka, 535-8585, Japan

    Juan M. Corchado

Authors
  1. Krishna Patel
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  2. Chintan Bhatt
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  3. Juan M. Corchado
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Corresponding author

Correspondence to Krishna Patel .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de València, Valencia, Valencia, Spain

    Vicente Julián

  2. ISEP/GECAD, Porto, Portugal

    João Carneiro

  3. Científico Universidad de Salamanca, AIR Institute Villamayor, Parque, Salamanca, Spain

    Ricardo S. Alonso

  4. Edificio I+D+i (24.2), University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  5. Departamento de Informática, University of Minho, Braga, Portugal

    Paulo Novais

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Patel, K., Bhatt, C., Corchado, J.M. (2023). Automatic Detection of Oil Spills from SAR Images Using Deep Learning. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_6

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