Citation: | XU Jin, LI Bo, CHEN Rong, MA Long, ZHAO Zhiqiang, LI Xidong. A review on marine radar oil spill monitoring technology[J]. Chinese Journal of MARINE ENVIRONMENTAL SCIENCE, 2023, 42(5): 805-812. DOI: 10.12111/j.mes.2022-x-0306 |
Oil spill ranks first in terms of occurrence frequency, pollution degree and long-term negative effects in marine environmental pollution. To minimize the losses after the oil spill accident, advanced technology for monitoring the oil spill in real-time is an important guarantee for emergency treatment and rapid recovery of marine ecological environment. The marine radar oil spill monitoring technology has the advantage of assisting emergency response all-day and all-weather. It has gradually attracted attention in the past decade. The marine radar oil spill monitoring technology is summarized and analyzed from the aspects of data preprocessing, effective region extraction, oil film segmentation. The oil spill identification method based on image texture features, machine learning and adaptive threshold is the mainstream in marine radar oil spill monitoring technology. With the continuous accumulation of the image samples, marine radar oil spill semantic segmentation using deep learning is the main direction in the future.
[1] |
KIEU H T, LAW A W K. Remote sensing of coastal hydro-environment with portable unmanned aerial vehicles (pUAVs) a state-of-the-art review[J]. Journal of Hydro-Environment Research, 2021, 37: 32-45. doi: 10.1016/j.jher.2021.04.003
|
[2] |
OLIVEIRA L G, ARAÚJO K C, BARRETO M C, et al. Applications of chemometrics in oil spill studies[J]. Microchemical Journal, 2021, 166: 106216. doi: 10.1016/j.microc.2021.106216
|
[3] |
胡后波, 王 庆. 中国南海最大油污案调解结案[J]. 中国审判, 2006 (8): 26-29.
|
[4] |
YANG Y Q, LI Y, LI J, et al. The influence of Stokes drift on oil spills: Sanchi oil spill case[J]. Acta Oceanologica Sinica, 2021, 40(10): 30-37. doi: 10.1007/s13131-021-1889-9
|
[5] |
邴 磊. 基于遥感和GIS的海上溢油风险识别及区划研究[D]. 烟台: 中国科学院大学(中国科学院烟台海岸带研究所), 2019.
|
[6] |
DEARDEN C, CULMER T, BROOKE R. Performance measures for validation of oil spill dispersion models based on satellite and coastal data[J]. IEEE Journal of Oceanic Engineering, 2022, 47(1): 126-140. doi: 10.1109/JOE.2021.3099562
|
[7] |
DASARI K, ANJANEYULU L, NADIMIKERI J. Application of C-band sentinel-1A SAR data as proxies for detecting oil spills of Chennai, East Coast of India[J]. Marine Pollution Bulletin, 2022, 174: 113182. doi: 10.1016/j.marpolbul.2021.113182
|
[8] |
SONG D M, DING Y X, LI X F, et al. Ocean oil spill classification with RADARSAT-2 SAR based on an optimized wavelet neural network[J]. Remote Sensing, 2017, 9(8): 799. doi: 10.3390/rs9080799
|
[9] |
ANGELLIAUME S, CEAMANOS X, VIALLEFONT-ROBINET F, et al. Hyperspectral and radar airborne imagery over controlled release of oil at sea[J]. Sensors, 2017, 17(8): 1772. doi: 10.3390/s17081772
|
[10] |
GALLEGO A J, GIL P, PERTUSA A, et al. Segmentation of oil spills on side-looking airborne radar imagery with autoencoders[J]. Sensors, 2018, 18(3): 797. doi: 10.3390/s18030797
|
[11] |
李 博, 潘新祥, 徐 进, 等. 船载雷达遥感图像的油膜快速识别方法[J]. 海洋环境科学, 2022, 41(5): 799-806.
|
[12] |
TENNYSON E J. Shipboard navigational radar as an oil spill tracking tool - a preliminary assessment [C]//Proceedings of the Annual Meeting for the OCEANS. Baltimore, MD, USA: IEEE, 1988.
|
[13] |
TENNYSON E J. Method of detecting oil spills at sea using a shipborne navigational radar[J]. Marine Pollution Bulletin, 1990, 21(11): 551.
|
[14] |
ATANASSOV V, MLADENOV L, RANGELOV R, et al. Observation of oil slicks on the sea surface by using marine navigation radar[C]//[Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management. Espoo, Finland: IEEE, 1991.
|
[15] |
GANGESKAR R. Automatic oil-spill detection by marine X-band radars - New system based on capturing and processing digitized radar images: Ready for extensive tests in October[J]. Sea Technology, 2004, 45(8): 40-45.
|
[16] |
楚晓亮, 徐 铭, 王 峰, 等. 利用X波段雷达提取海浪信息的分析[J]. 中国海洋大学学报, 2011, 41(5): 110-113.
|
[17] |
NOST E, EGSET C N. Oil spill detection system-results from field trials[C]//OCEANS 2006. Boston: IEEE, 2006.
|
[18] |
EGSET C N, NOST E. Oil spill detection system based on marine X-band radar[J]. Sea Technology, 2007, 48(4): 41-45.
|
[19] |
ZHU X Y, LI Y, FENG H Y, et al. Oil spill detection method using X-band marine radar imagery[J]. Journal of Applied Remote Sensing, 2015, 9(1): 095985. doi: 10.1117/1.JRS.9.095985
|
[20] |
LIU P, LI Y, XU J, et al. Adaptive enhancement of X-band marine radar imagery to detect oil spill segments[J]. Sensors, 2017, 17(10): 2349. doi: 10.3390/s17102349
|
[21] |
XU J, LIU P, WANG H, et al. Marine radar oil spill monitoring technology based on dual-threshold and C-V level set methods[J]. Journal of the Indian Society of Remote Sensing, 2018, 46(12): 1949-1961. doi: 10.1007/s12524-018-0853-4
|
[22] |
XU J, WANG H X, CUI C, et al. Oil spill segmentation in ship-borne radar images with an improved active contour model[J]. Remote Sensing, 2019, 11(14): 1698. doi: 10.3390/rs11141698
|
[23] |
XU J, CUI C, FENG H Y, et al. Marine radar oil-spill monitoring through local adaptive thresholding[J]. Environmental Forensics, 2019, 20(2): 196-209. doi: 10.1080/15275922.2019.1597781
|
[24] |
LIU P, LI Y, LIU B X, et al. Semi-automatic oil spill detection on x-band marine radar images using texture analysis, machine learning, and adaptive thresholding[J]. Remote Sensing, 2019, 11(7): 756. doi: 10.3390/rs11070756
|
[25] |
LIU P, LI Y, XU J, et al. Oil spill extraction by X-band marine radar using texture analysis and adaptive thresholding[J]. Remote Sensing Letters, 2019, 10(6): 583-589. doi: 10.1080/2150704X.2019.1587197
|
[26] |
XU J, WANG H X, CUI C, et al. Oil spill monitoring of shipborne radar image features using SVM and local adaptive threshold[J]. Algorithms, 2020, 13(3): 69. doi: 10.3390/a13030069
|
[27] |
XU J, JIA B Z, PAN X X, et al. Hydrographic data inspection and disaster monitoring using shipborne radar small range images with electronic navigation chart[J]. PeerJ Computer Science, 2020, 6: e290. doi: 10.7717/peerj-cs.290
|
[28] |
XU J, PAN X X, JIA B Z, et al. Oil spill detection using LBP feature and K-means clustering in shipborne radar image[J]. Journal of Marine Science and Engineering, 2021, 9(1): 65. doi: 10.3390/jmse9010065
|
[29] |
XU J, PAN X X, WU X R, et al. Oil spill discrimination of multi-time-domain shipborne radar images using active contour model[J]. Geoscience Letters, 2021, 8(1): 7. doi: 10.1186/s40562-021-00178-8
|
[30] |
李 颖, 徐 进, 冯海洋, 等. 航海雷达溢油监测轨迹回放模型[J]. 海洋环境科学, 2014, 33(2): 322-326.
|
[31] |
徐 进, 李 博, 崔 璨, 等. 航海雷达溢油监测技术研究[J]. 海洋环境科学, 2018, 37(1): 125-129. doi: 10.13634/j.cnki.mes.2018.01.019
|
[32] |
徐 进, 李 颖, 杨文玉, 等. 航海雷达溢油信息采集分析模型[J]. 海洋环境科学, 2016, 35(2): 264-269. doi: 10.13634/j.cnki.mes.2016.02.019
|
[33] |
LI B, XU J, PAN X X, et al. Marine oil spill detection with X-band shipborne radar using GLCM, SVM and FCM[J]. Remote Sensing, 2022, 14(15): 3715. doi: 10.3390/rs14153715
|
[34] |
CHEN R, LI B, JIA B Z, et al. Oil spill identification in X-band marine radar image using K-means and texture feature[J]. PeerJ Computer Science, 2022, 8: e1133. doi: 10.7717/peerj-cs.1133
|
[35] |
ZUIDERVELD K. Contrast limited adaptive histogram equalization[M]//HECKBERT R S. Graphics Gems IV. San Diego: Academic Press Professional, Inc. , 1994: 474-485.
|
[36] |
OJALA T, PIETIKÄINEN M, HARWOOD D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 29(1): 51-59. doi: 10.1016/0031-3203(95)00067-4
|
[37] |
HARALICK R M, SHANMUGAM K, DINSTEIN I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610-621. doi: 10.1109/TSMC.1973.4309314
|
[38] |
NIBLACK W. An introduction to digital image processing[M]. Englewood Cliffs, NJ: Prentice Hall, 1986.
|
[39] |
SAUVOLA J, PIETIKÄINEN M. Adaptive document image binarization[J]. Pattern Recognition, 2000, 33(2): 225-236. doi: 10.1016/S0031-3203(99)00055-2
|
[40] |
PHANSALKAR N, MORE S, SABALE A, et al. Adaptive local thresholding for detection of nuclei in diversity stained cytology images[C]//Proceedings of the 2011 International Conference on Communications and Signal Processing. Kerala, India: IEEE, 2011.
|
[41] |
KITTLER J, ILLINGWORTH J. Minimum error thresholding[J]. Pattern Recognition, 1986, 19(1): 41-47. doi: 10.1016/0031-3203(86)90030-0
|
[42] |
CHAN T F, VESE L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2): 266-277. doi: 10.1109/83.902291
|
[43] |
LI C M, KAO C Y, GORE J C, et al. Minimization of regionscalable fitting energy for image segmentation[J]. IEEE Trans Image Process, 2008, 17(10): 1940-1949. doi: 10.1109/TIP.2008.2002304
|
[44] |
李传龙. 基于水平集和模糊聚类方法的图像分割技术研究[D]. 大连: 大连海事大学, 2012.
|