A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

Author(s):  
Wandi Wang ◽  
Hui Sheng ◽  
Yanlong Chen ◽  
Shanwei Liu ◽  
Jijun Mao ◽  
...  
2021 ◽  
Vol 9 (3) ◽  
pp. 279
Author(s):  
Zhehao Yang ◽  
Weizeng Shao ◽  
Yuyi Hu ◽  
Qiyan Ji ◽  
Huan Li ◽  
...  

Marine oil spills occur suddenly and pose a serious threat to ecosystems in coastal waters. Oil spills continuously affect the ocean environment for years. In this study, the oil spill caused by the accident of the Sanchi ship (2018) in the East China Sea was hindcast simulated using the oil particle-tracing method. Sea-surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF), currents simulated from the Finite-Volume Community Ocean Model (FVCOM), and waves simulated from the Simulating WAves Nearshore (SWAN) were employed as background marine dynamics fields. In particular, the oil spill simulation was compared with the detection from Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) images. The validation of the SWAN-simulated significant wave height (SWH) against measurements from the Jason-2 altimeter showed a 0.58 m root mean square error (RMSE) with a 0.93 correlation (COR). Further, the sea-surface current was compared with that from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2), yielding a 0.08 m/s RMSE and a 0.71 COR. Under these circumstances, we think the model-simulated sea-surface currents and waves are reliable for this work. A hindcast simulation of the tracks of oil slicks spilled from the Sanchi shipwreck was conducted during the period of 14–17 January 2018. It was found that the general track of the simulated oil slicks was consistent with the observations from the collected GF-3 SAR images. However, the details from the GF-3 SAR images were more obvious. The spatial coverage of oil slicks between the SAR-detected and simulated results was about 1 km2. In summary, we conclude that combining numerical simulation and SAR remote sensing is a promising technique for real-time oil spill monitoring and the prediction of oil spreading.


1998 ◽  
Author(s):  
Julio C. Jacobo Berlles ◽  
Marta Mejail ◽  
Alejandro C. Frery

Author(s):  
Georgios Orfanidis ◽  
Konstantinos Ioannidis ◽  
Konstantinos Avgerinakis ◽  
Stefanos Vrochidis ◽  
Ioannis Kompatsiaris

Author(s):  
Martine M. Espeseth ◽  
Cathleen E. Jones ◽  
Benjamin Holt ◽  
Camilla Brekke ◽  
Stine Skrunes

2020 ◽  
Vol 12 (6) ◽  
pp. 944 ◽  
Author(s):  
Jin Zhang ◽  
Hao Feng ◽  
Qingli Luo ◽  
Yu Li ◽  
Jujie Wei ◽  
...  

Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%.


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