Monitoring of Oil Spill Trajectories With COSMO-SkyMed X-Band SAR Images and Model Simulation

Cheng, Yongcun; Liu, Bingqing; Li, Xiaofeng; Nunziata, Ferdinando; Xu, Qing; Ding, Xuhui; Migliaccio, M.; Pichel, William G.

doi:10.1109/jstars.2014.2341574

Cited by 53 publications

(27 citation statements)

References 28 publications

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“…In addition to the general advantages of the aforementioned high spatial resolution and polarimetry, advanced SARs have constellation configuration design. The Cosmo-SkyMed, TSX/TanDEM-X (TDX) and Sentinel-1A/1B missions, as well as the forthcoming Radarsat Constellation Mission (RCM), all operate in constellations, which significantly reduces the temporal intervals of SAR data acquisition and therefore enhances the capture of dynamic sea surface information [21][22][23]. In particular, TSX can cooperate with its twin, TDX, to achieve along-track interferometry and to retrieve sea surface currents in high spatial resolution from space [24].During the development of spaceborne SAR, the wave mode of the ESA's SAR missions has played an interesting role in ocean observations.…”

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confidence: 99%

Capabilities of Chinese Gaofen-3 Synthetic Aperture Radar in Selected Topics for Coastal and Ocean Observations

Zhang

Huang

et al. 2018

Remote Sensing

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Gaofen-3 (GF-3), the first Chinese spaceborne synthetic aperture radar (SAR) in C-band for civil applications, was launched on August 2016. Some studies have examined the use of GF-3 SAR data for ocean and coastal observations, but these studies generally focus on one particular application. As GF-3 has been in operation over two years, it is essential to evaluate its performance in ocean observation, a primary goal of the GF-3 launch. In this paper, we offer an overview demonstrating the capabilities of GF-3 SAR in ocean and coastal observations by presenting several representative cases, i.e., the monitoring of intertidal flats, offshore tidal turbulent wakes and oceanic internal waves, to highlight the GF-3's full polarimetry, high spatial resolution and wide-swath imaging advantages. Moreover, we also present a detailed analysis of the use of GF-3 quad-polarization data for sea surface wind retrievals and wave mode data for sea surface wave retrievals. The case studies and statistical analysis suggest that GF-3 has good ocean and coastal monitoring capabilities, though further improvements are possible, particularly in radiometric calibration and stable image quality.kilometers. Wide-swath or ScanSAR images generally have a spatial resolution of tens of meters and, more importantly, can map a large area of the open sea and coast, which makes them particularly suitable for studying meso-scale oceanic and atmospheric processes, e.g., by mapping the distribution of internal ocean waves [2], observing atmospheric solitons [3], estimating the wind speed of tropical cyclones [4], and measuring sea surface velocity [5].The year 2007 marks an important advance in the development of spaceborne SAR; two X-band spaceborne SAR, the TerraSAR-X (TSX) and Cosmo-SkyMed and the C-band SAR, Radarsat-2 (R2), were launched. Compared with previous spaceborne SAR missions, the new generation of SAR sensors has several advantages. One advantage is that the new generation can acquire images with a high spatial resolution of up to 1 m in spotlight mode [6][7][8]. This offers a unique opportunity to detect targets in the ocean and coast, e.g., for ship detection [9][10][11]. The other advantage is that these SAR sensors have polarimetric capabilities of acquiring data in different polarization combinations of VV, HH, VH and HV. These SAR polarimetric data are widely exploited for oil spill detection or classification [12][13][14], analysis of objects scattering or their classification in coastal intertidal flats [15][16][17], and sea ice detection and classification [18][19][20]. In addition to the general advantages of the aforementioned high spatial resolution and polarimetry, advanced SARs have constellation configuration design. The Cosmo-SkyMed, TSX/TanDEM-X (TDX) and Sentinel-1A/1B missions, as well as the forthcoming Radarsat Constellation Mission (RCM), all operate in constellations, which significantly reduces the temporal intervals of SAR data acquisition and therefore enhances the capture of dynamic sea surface information ...

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confidence: 99%

Capabilities of Chinese Gaofen-3 Synthetic Aperture Radar in Selected Topics for Coastal and Ocean Observations

Zhang

Huang

et al. 2018

Remote Sensing

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“…The function is given by Equation (12) (12) where the meaning of x jmax , x jmin and ∆x 0i are the same as above-mentioned explained.…”

Section: Parameter Definitionmentioning

confidence: 99%

“…Satellite remote sensing technology has been widely used in oil spill observations due to its frequent large coverage and relatively low cost [5][6][7][8][9][10][11]. Among remote sensing sensors, synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of a particular oil spill under moderate wind speed (4-12 m/s) weather conditions, day and night [12]. However, it is a challenge to distinguish oil spills from other lookalike natural phenomena (biogenic slicks, upwelling, low wind areas, rain cells, shear zones, internal waves, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Efforts have been devoted to improve oil spill detection and classification. Nowadays, there is a general consensus that the extra information provided by the polarimetric SAR (Pol-SAR) data enhances the capabilities of identifying and classifying the A 12 High Low Skrunes, Brekke, and Eltoft (2014) [8] α Low High Minchew, Jones, and Holt (2012) [25] ν High Low Skrunes, Brekke, and Eltoft (2014) [8] [15], Skrunes Brekke, and Eltoft (2014) [8] Over the past several decades, artificial neural network algorithms have been widely used in remote sensing image classification [27][28][29], due to their good self-organization [30][31][32], self-learning [33,34], and self-adaptive abilities [35]. Among these, BP (Back Propagation) neural network is one of the first widely used network models due to its simple and easy to implement training in initially stage.…”

Section: Introductionmentioning

confidence: 99%

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Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

Song

Ding

et al. 2017

Remote Sensing

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Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote ocean oil spill classification accuracy, we developed a new oil spill identification method by combining multiple fully polarimetric SAR features data with an optimized wavelet neural network classifier (WNN). Two sets of RADARSAT-2 fully polarimetric SAR data are applied to test the validity of the developed method. The experimental results show that: (1) the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of ocean oil spill, in comparison to the classification results based on a common un-optimized WNN classifier; and (2) the joint use of the multiple fully Pol-SAR features as the inputs of the classifier can achieve better classification result than that only with single fully Pol-SAR feature. The developed method can improve classification accuracy by 4.96% and 7.75%, compared with the classification results with un-optimized WNN and only with one single fully polarimetric SAR feature. The classification overall accuracy based on the proposed approach can reach 97.67%. Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill.

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“…State-of-the-art satellite imagery, especially Synthetic Aperture Radar (SAR), enables to spot oil slicks in the early stages of an accident and to monitor systematic pollution over major shipping routes (Vijayakumar and Santhi 2015). Moreover, the trajectory of the oil slick may be obtained through multi-temporal imagery; thereby assisting in drift prediction modelling that may be used to facilitate clean-up operations and warning systems (Cheng et al 2014). The backscattered microwave energy of the electromagnetic spectrum from a radar target (e.g.…”

Section: Introductionmentioning

confidence: 99%

Pre-Launch Assessment of RADARSAT Constellation Mission Medium Resolution Modes for Sea Oil Slicks and Lookalike Discrimination

Dabboor

Singha

Montpetit

et al. 2019

Canadian Journal of Remote Sensing

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The RADARSAT Constellation Mission (RCM) is a Canadian Synthetic Aperture Radar (SAR) mission, providing C-band SAR data continuity of the RADARSAT-1 and RADARSAT-2 satellite missions. The RCM which was recently launched (June 2019) will provide compact polarimetry (CP) as a polarization option for all non-quad-polarization imaging modes. Maritime pollution monitoring is one of the RCM core applications. Thus, the purpose of this study is to provide an initial evaluation of the expected potential of the RCM mission for maritime pollution monitoring. Herein, we investigate simulated CP SAR data of three RCM Medium Resolution (MR) SAR modes for oil slick detection and discrimination from lookalike (LA) features. Results of our study indicated that the steeper radar incidence angle seems to be preferable for oil slick detection in all examined modes. The ScanSAR 50 m resolution (SC50M) mode showed the highest overall performance in terms of LA and Emulsion (EM) classification (95.4%). This was also valid for LA and Crude Oil (CO) classification (96.7%). The performance of the SC50M mode was slightly higher than that of the ScanSAR 30 m resolution (SC30M) mode. R ESUM EARTICLE HISTORY

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Monitoring of Oil Spill Trajectories With COSMO-SkyMed X-Band SAR Images and Model Simulation

Cited by 53 publications

References 28 publications

Capabilities of Chinese Gaofen-3 Synthetic Aperture Radar in Selected Topics for Coastal and Ocean Observations

Capabilities of Chinese Gaofen-3 Synthetic Aperture Radar in Selected Topics for Coastal and Ocean Observations

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

Pre-Launch Assessment of RADARSAT Constellation Mission Medium Resolution Modes for Sea Oil Slicks and Lookalike Discrimination

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