scholarly journals A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface

2020 ◽  
Author(s):  
Oktay Karakus ◽  
Igor Rizaev ◽  
Alin Achim
Keyword(s):  
Simulation Study ◽  
Sea Surface ◽  
Sar Images ◽  
Proximal Operator

scholarly journals Revisit of a Case Study of Spilled Oil Slicks Caused by the Sanchi Accident (2018) in the East China Sea

2021 ◽  
Vol 9 (3) ◽  
pp. 279
Author(s):  
Zhehao Yang ◽  
Weizeng Shao ◽  
Yuyi Hu ◽  
Qiyan Ji ◽  
Huan Li ◽  
...  
Keyword(s):  
East China Sea ◽  
Oil Spill ◽  
Oil Spills ◽  
Ocean Model ◽  
Sea Surface ◽  
East China ◽  
The East China Sea ◽  
Sar Images ◽  
China Sea ◽  
Spatial Coverage

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.

scholarly journals Toward Automated Identification of Sea Surface Temperature Front Signatures in Radarsat-2 Images

2012 ◽  
Vol 29 (1) ◽  
pp. 89-102 ◽  
Author(s):  
Chris T. Jones ◽  
Todd D. Sikora ◽  
Paris W. Vachon ◽  
John Wolfe
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Canadian Forces ◽  
Automated Identification ◽  
Sar Images ◽  
Accurate Identification ◽  
Sst Front ◽  
Atmospheric Phenomena ◽  
Sst Fronts

Abstract The Canadian Forces Meteorology and Oceanography Center produces a near-daily ocean feature analysis, based on sea surface temperature (SST) images collected by spaceborne radiometers, to keep the fleet informed of the location of tactically important ocean features. Ubiquitous cloud cover hampers these data. In this paper, a methodology for the identification of SST front signatures in cloud-independent synthetic aperture radar (SAR) images is described. Accurate identification of ocean features in SAR images, although attainable to an experienced analyst, is a difficult process to automate. As a first attempt, the authors aimed to discriminate between signatures of SST fronts and those caused by all other processes. Candidate SST front signatures were identified in Radarsat-2 images using a Canny edge detector. A feature vector of textural and contextual measures was constructed for each candidate edge, and edges were validated by comparison with coincident SST images. Each candidate was classified as being an SST front signature or the signature of another process using logistic regression. The resulting probability that a candidate was correctly classified as an SST front signature was between 0.50 and 0.70. The authors concluded that improvement in classification accuracy requires a set of measures that can differentiate between signatures of SST fronts and those of certain atmospheric phenomena and that a search for such measures should include a wider range of computational methods than was considered. As such, this work represents a step toward the goal of a general ocean feature classification algorithm.

Fitting a statistical model to SIR-C SAR images of the sea surface

2004 ◽  
Vol 40 (4) ◽  
pp. 1179-1190 ◽  
Author(s):  
A. Fusco ◽  
C. Galdi ◽  
G. Ricci ◽  
M. Tesauro
Keyword(s):  
Statistical Model ◽  
Sea Surface ◽  
Sar Images

scholarly journals A Wind Speed Retrieval Model for Sentinel-1A EW Mode Cross-Polarization Images

2019 ◽  
Vol 11 (2) ◽  
pp. 153 ◽  
Author(s):  
Yuan Gao ◽  
Changlong Guan ◽  
Jian Sun ◽  
Lian Xie
Keyword(s):  
Wind Speed ◽  
Incidence Angle ◽  
Surface Wind ◽  
European Space Agency ◽  
Sea Surface ◽  
Cross Polarization ◽  
Retrieval Model ◽  
Sar Images ◽  
Wind Speeds ◽  
Sea Surface Wind

In contrast to co-polarization (VV or HH) synthetic aperture radar (SAR) images, cross-polarization (CP for VH or HV) SAR images can be used to retrieve sea surface wind speeds larger than 20 m/s without knowing the wind directions. In this paper, a new wind speed retrieval model is proposed for European Space Agency (ESA) Sentinel-1A (S-1A) Extra-Wide swath (EW) mode VH-polarized images. Nineteen S-1A images under tropical cyclone condition observed in the 2016 hurricane season and the matching data from the Soil Moisture Active Passive (SMAP) radiometer are collected and divided into two datasets. The relationships between normalized radar cross-section (NRCS), sea surface wind speed, wind direction and radar incidence angle are analyzed for each sub-band, and an empirical retrieval model is presented. To correct the large biases at the center and at the boundaries of each sub-band, a corrected model with an incidence angle factor is proposed. The new model is validated by comparing the wind speeds retrieved from S-1A images with the wind speeds measured by SMAP. The results suggest that the proposed model can be used to retrieve wind speeds up to 35 m/s for sub-bands 1 to 4 and 25 m/s for sub-band 5.

Detection of Extreme Waves in SAR Images and Radar-Image Sequences

2002 ◽  
Author(s):  
Heiko Dankert ◽  
Jochen Horstmann ◽  
Wolfgang Rosenthal
Keyword(s):  
Image Sequences ◽  
Sea Surface ◽  
Radar Image ◽  
Data Sets ◽  
Offshore Platforms ◽  
Extreme Waves ◽  
Sar Images ◽  
Wave Groups ◽  
Resonance Frequencies ◽  
Spatio Temporal

Extreme waves are often enclosed by other waves which are also higher than the average. These wave groups have to be taken into account for instance for the design of offshore platforms, breakwaters or ships, because successive high waves can cause more damage on those structures than the same waves separated by smaller waves. Further they can excite the resonance frequencies of moored structures like platforms due to non-linear effects or cause capsize. They are therefore of interest for engineers and scientists (e.g. Goda 1983). A method is presented to localize wave groups spatial and spatio-temporal utilizing synthetic aperture radar (SAR) images and nautical radar-image sequences. The approach to detect wave groups is based on the detection of the wave envelope. It is assumed that the sea surface elevation can be treated as a Gaussian process. The method is applied to SAR images acquired by the European satellite ERS-1 and to radar-image sequences recorded by tower-based nautical radars. In contrast to 1D sensors like buoys the SAR records an image and gives therefore a 2D description of the sea surface by measuring the radar backscatter from the sea surface. The measurements taken by a nautical radar provide the possibility to record time series of images and therefore to get a 3D description of the sea surface. Radar-image sequences are acquired by recording the spatial and temporal evolution of the sea surface backscatter, which is modulated through the surface wave field. Nautical radar-image sequences allow to detect wave groups within a time span that makes it possible to start safety programs before the group reaches a platform. The existing data sets are exploited with respect to the recognition of extreme wave events.

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