Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

被引:5
|
作者
Liao, Lu [1 ,2 ]
Zhao, Qing [1 ]
Song, Wenyue [3 ]
机构
[1] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China
[2] Sichuan Bur Surveying Mapping & Geoinformat, Technol Serv Ctr Surveying & Mapping, Chengdu 610081, Peoples R China
[3] Anhui Univ, Sch Resources & Environm Engn, Hefei 230601, Peoples R China
关键词
oil film; water pollution; remote sensing; coastal area; deep learning; CLASSIFICATION;
D O I
10.3390/su151914504
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Healthy coasts have a high ecological service value. However, many coastal areas are faced with oil spill risks. The Synthetic Aperture Radar (SAR) remote sensing technique has become an effective tool for monitoring the oil spill risk in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative oil spill monitoring framework was established based on Polarimetric SAR (PolSAR) images and deep learning theory. Specifically, a DeepLabv3+-based semantic segmentation model was trained using 35 Sentinel-1 satellite images of oil films on the sea surface from maritime sectors in different regions all over the world, which not only considered the information from the PolSAR images but also meteorological conditions; then, the well-trained framework was deployed to identify the oil films in the Sentinel-1 images of Jiaozhou Bay from 2017 to 2019. The experimental results show that the detection accuracies of the proposed oil spill detection model were higher than 0.95. It was found that the oil films in Jiaozhou Bay were mainly concentrated in the vicinity of the waterways and coastal port terminals, that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, and that more than 80 percent of the oil spill events occurred at night, mainly coming from the illegal discharge of waste oil from ships. These data indicate that, in the future, the PolSAR technique will play a more important role in oil spill monitoring for Jiaozhou Bay due to its capability to capture images at night.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Marine oil spill detection using improved polarimetric feature based on polarization SAR image
    Wang, Dawei
    Song, Shasha
    Yang, Junfang
    Xu, Mingming
    Song, Dongmei
    Guo, Jie
    Wan, Jianhua
    Liu, Shanwei
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2024, 45 (03) : 911 - 929
  • [42] Large oil spill classification using SAR images based on spatial histogram
    Schvartzman, I.
    Havivi, S.
    Maman, S.
    Rotman, S. R.
    Blumberg, D. G.
    XXIII ISPRS CONGRESS, COMMISSION VIII, 2016, 41 (B8): : 1183 - 1186
  • [43] A modified FCM-based algorithm for oil spill detection in SAR images
    Zheng, YH
    Dong, HL
    Jiang, QS
    Li, J
    Environmental Informatics, Proceedings, 2005, : 346 - 351
  • [44] Multitask GANs for Oil Spill Classification and Semantic Segmentation Based on SAR Images
    Fan, Jianchao
    Liu, Chuan
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2023, 16 : 2532 - 2546
  • [45] Comparison of a massive and diverse collection of ensembles and other classifiers for oil spill detection in SAR satellite images
    D. Mera
    M. Fernández-Delgado
    J. M. Cotos
    J. R. R. Viqueira
    S. Barro
    Neural Computing and Applications, 2017, 28 : 1101 - 1117
  • [46] A NOVEL CHANGE DETECTION FRAMEWORK BASED ON DEEP LEARNING FOR THE ANALYSIS OF MULTI-TEMPORAL POLARIMETRIC SAR IMAGES
    De, Shaunak
    Pirrone, Davide
    Bovolo, Francesca
    Bruzzone, Lorenzo
    Bhattacharya, Avik
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 5193 - 5196
  • [47] Deep Learning-Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1
    Mestre-Quereda, Alejandro
    Lopez-Sanchez, Juan M.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2025, 63
  • [48] Comparison of a massive and diverse collection of ensembles and other classifiers for oil spill detection in SAR satellite images
    Mera, D.
    Fernandez-Delgado, M.
    Cotos, J. M.
    Viqueira, J. R. R.
    Barro, S.
    NEURAL COMPUTING & APPLICATIONS, 2017, 28 : S1101 - S1117
  • [49] SAR-based oil spill detection and impact assessment on coastal and marine environments
    Ozair, Muhammad
    Iqbal, Muhammad Farooq
    Mahmood, Irfan
    Naz, Saima
    ACTA OCEANOLOGICA SINICA, 2024, 43 (12) : 123 - 140
  • [50] SAR-based oil spill detection and impact assessment on coastal and marine environments
    Muhammad Ozair
    Muhammad Farooq Iqbal
    Irfan Mahmood
    Saima Naz
    Acta Oceanologica Sinica, 2024, 43 (12) : 123 - 140