Laser & Optoelectronics Progress, Volume. 60, Issue 24, 2412002(2023)

Oil Spill Detection Algorithm of a Fully Polarimetric SAR Based on Dual-EndNet

Dongmei Song1,2, Mingyue Wang1、*, Chengcong Hu3, Jie Zhang1,4, Bin Wang1, Shanwei Liu1, Dawei Wang1, and Bin Liu5
Author Affiliations
  • 1College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, Shandong, China
  • 2Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, Shandong, China
  • 3China National Logging Corporation, Beijing 100101, China
  • 4First Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, Shandong, China
  • 5Qingdao Marine Science and Technology Center, Qingdao 266237, Shandong, China
  • show less
    References(24)

    [1] Zacharias D C, Rezende K F O, Fornaro A. Offshore petroleum pollution compared numerically via algorithm tests and computation solutions[J]. Ocean Engineering, 151, 191-198(2018).

    [2] Wang B, Shao Q F, Song D M et al. A spectral-spatial features integrated network for hyperspectral detection of marine oil spill[J]. Remote Sensing, 13, 1568(2021).

    [3] Krestenitis M, Orfanidis G, Ioannidis K et al. Oil spill identification from satellite images using deep neural networks[J]. Remote Sensing, 11, 1762(2019).

    [4] Konik M, Bradtke K. Object-oriented approach to oil spill detection using ENVISAT ASAR images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 118, 37-52(2016).

    [5] Salberg A B, Rudjord Ø, Solberg A H S. Oil spill detection in hybrid-polarimetric SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 6521-6533(2014).

    [6] Du Y L, Cui J H, Wei Q M et al. Marine oil-spill detection in multi-polarization image-based SAR on improved FCN[J]. Laser & Optoelectronics Progress, 59, 0415005(2022).

    [7] Topouzelis K, Psyllos A. Oil spill feature selection and classification using decision tree forest on SAR image data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 135-143(2012).

    [8] Shu Y M, Li J, Yousif H et al. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring[J]. Remote Sensing of Environment, 114, 2026-2035(2010).

    [9] Kapustin I A, Shomina O V, Ermoshkin A V et al. On capabilities of tracking marine surface currents using artificial film slicks[J]. Remote Sensing, 11, 840(2019).

    [10] Fiscella B, Giancaspro A, Nirchio F et al. Oil spill detection using marine SAR images[J]. International Journal of Remote Sensing, 21, 3561-3566(2000).

    [11] Nirchio F, Sorgente M, Giancaspro A et al. Automatic detection of oil spills from SAR images[J]. International Journal of Remote Sensing, 26, 1157-1174(2005).

    [12] Li Z H, Chen L, Zhang B C et al. SAR image oil spill detection based on maximum entropy threshold segmentation[J]. Journal of Signal Processing, 35, 1111-1117(2019).

    [13] Fan J C, Wang T. HJ-1 satellite remote sensing image segmentation in the oil spill of Mexico Gulf base on the non-negative matrix factorization and support vector machine[J]. Marine Environmental Science, 34, 441-446(2015).

    [14] Wei X G, Cao L, Tian S et al. Remote sensing target detection based on multi-level self-attention enhancement[J]. Laser & Optoelectronics Progress, 60, 2028004(2023).

    [15] Dai Y, Yi B S, Xiao J S et al. Object detection of remote sensing image based on improved rotation region proposal network[J]. Acta Optica Sinica, 40, 0111020(2020).

    [16] Zheng Z S, Liu B, Lu P et al. Spectral classification and characteristic spectral analysis of nearshore aquatic plants based on AlexNet[J]. Chinese Journal of Lasers, 50, 0211001(2023).

    [17] Yekeen S T, Balogun A, Wan Yusof K B. A novel deep learning instance segmentation model for automated marine oil spill detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 190-200(2020).

    [18] Zeng K, Wang Y X. A deep convolutional neural network for oil spill detection from spaceborne SAR images[J]. Remote Sensing, 12, 1015(2020).

    [19] Zhu Q Q, Zhang Y N, Li Z Q et al. Oil spill contextual and boundary-supervised detection network based on marine SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5213910(2022).

    [20] Li G N, Li Y, Hou Y C et al. Marine oil slick detection using improved polarimetric feature parameters based on polarimetric synthetic aperture radar data[J]. Remote Sensing, 13, 1607(2021).

    [21] Fei G, Teng H, Wang J et al. Dual-branch deep convolution neural network for polarimetric SAR image classification[J]. Applied Sciences, 7, 447(2017).

    [22] Fei G, Teng H, Jun W et al. Dual-branch deep convolution neural network for polarimetric SAR image classification[J]. Applied Sciences, 7, 447(2017).

    [23] Zhou Z H[M]. Machine learning(2016).

    [24] Skrunes S, Brekke C, Eltoft T. Characterization of marine surface slicks by radarsat-2 multipolarization features[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 5302-5319(2014).

    Tools

    Get Citation

    Copy Citation Text

    Dongmei Song, Mingyue Wang, Chengcong Hu, Jie Zhang, Bin Wang, Shanwei Liu, Dawei Wang, Bin Liu. Oil Spill Detection Algorithm of a Fully Polarimetric SAR Based on Dual-EndNet[J]. Laser & Optoelectronics Progress, 2023, 60(24): 2412002

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Feb. 17, 2023

    Accepted: Apr. 4, 2023

    Published Online: Nov. 27, 2023

    The Author Email: Wang Mingyue (s20160047@s.upc.edu.cn)

    DOI:10.3788/LOP230660

    Topics