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Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging.

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  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE
    • بيانات النشر:
      Original Publication: Basel, Switzerland : MDPI, c2000-
    • نبذة مختصرة :
      Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models-DRSNet, CNN-Visual Transformer, and GCN-conducting a comprehensive analysis to evaluate the advantages and limitations of each model.
    • References:
      IEEE Trans Neural Netw Learn Syst. 2022 Dec;33(12):6999-7019. (PMID: 34111009)
      Mar Pollut Bull. 2023 May;190:114834. (PMID: 36934487)
      Mar Pollut Bull. 2023 Jul;192:114981. (PMID: 37209663)
    • Grant Information:
      YQZC202205 Open Fund of Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas（Yangtze University）; XSTS-202101 Open Fund of Xi'an Key Laboratory of Tight oil (Shale oil); D20201304 Development of the Scientific Research Projects of the Hubei Provincial Department of Education
    • Contributed Indexing:
      Keywords: artificial neural network; hyperspectral image; offshore oil spill
    • الموضوع:
      Date Created: 20240123 Latest Revision: 20240129
    • الموضوع:
      20240129
    • الرقم المعرف:
      PMC10819121
    • الرقم المعرف:
      10.3390/s24020411
    • الرقم المعرف:
      38257504