Detection of oil slick signatures in SAR images by fusion of hysteresis thresholding responses

Kanaa, Thomas; Tonyé, Emmanuel; Mercier, Grégoire; Onana, V.P.; Ngono, J. Mvogo; Frison, Patrice; Rudant, Jean-Paul; Garello, R.

doi:10.1109/igarss.2003.1294573

Cited by 26 publications

(17 citation statements)

References 15 publications

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“…For each of the 14 dates, these three classes are distinguished with a supervised thresholding algorithm. Both VH and VV polarization images are fed into the algorithm, which is based on hysteresis thresholding [37,46]. In our study, the method combines both spatial and temporal dimensions.…”

Section: Flood Detectionmentioning

confidence: 99%

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Cazals¹,

Rapinel

Frison³

et al. 2016

Remote Sensing

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Abstract:In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

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Section: Flood Detectionmentioning

confidence: 99%

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Cazals¹,

Rapinel

Frison³

et al. 2016

Remote Sensing

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show abstract

“…The power-tomean ratio being a general measure of homogeneity is used to adjust the threshold. Various authors (Solberg and Solberg, 1996;Solberg and Volden, 1997;Kanna et al, 2003;Nirchio et al, 2005) International Journal of Applied Earth Observation and Geoinformation 11 (2009) This paper introduces a modified formula for the fractal box counting dimension. The method is based on utilization of the probability distribution formula in the fractal box count.…”

Section: Introductionmentioning

confidence: 98%

Modification of fractal algorithm for oil spill detection from RADARSAT-1 SAR data

Marghany

Cracknell

Hashim

2009

International Journal of Applied Earth Observation and Geoinfor

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“…The power-to-mean ratio is a good measure of homogeneity used to adjust the threshold. Various authors (Solberg and Solberg 1996, Solberg and Volden 1997, Kanna et al 2003, Nirchio et al 2005 have reported that for RADARSAT-1 SAR ScanSAR narrow mode, thresholding is done at three different scales. Using three different scales, however, did not work well for ENVISAT satellite data.…”

Section: Introductionmentioning

confidence: 99%

Comparison between radarsat-1 SAR different data modes for oil spill detection by a fractal box counting algorithm

Marghany¹,

Cracknell²,

Hashim³

2009

International Journal of Digital Earth

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This work presents a modified formula for the fractal box counting dimension. The method is based on the utilisation of the probability distribution formula in the fractal box count. The purpose of this method is to use it for the discrimination of oil spill areas from the surrounding features, e.g. sea surface and look-alikes, using RADARSAT-1 SAR Wide beam mode (W1), Standard beam mode (S2) and Standard beam mode (S1) data acquisition under different wind speeds. The results show that the new formula is able to discriminate between oil spills and look-alike areas. The results also illustrate that the new fractal formula identifies well the deficiency of oil spills in pairs of S2 data. Further, there are no significant differences between fractal values of look-alikes, low wind zone, and current shear features in different beam modes for acquisition of RADARSAT-1 SAR data. The W1 mode data, however, show an error standard deviation of 0.002, thus performing a better discrimination of oil spills than the S1 and S2 mode data.

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Detection of oil slick signatures in SAR images by fusion of hysteresis thresholding responses

Cited by 26 publications

References 15 publications

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

Modification of fractal algorithm for oil spill detection from RADARSAT-1 SAR data

Comparison between radarsat-1 SAR different data modes for oil spill detection by a fractal box counting algorithm

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