2007
DOI: 10.1016/j.isprsjprs.2007.05.003
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Detection and discrimination between oil spills and look-alike phenomena through neural networks

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Cited by 160 publications
(105 citation statements)
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References 26 publications
(45 reference statements)
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“…For this analysis, images were segmented into groups of pixels with similar spectral and spatial characteristics. A multiresolution segmentation algorithm that was previously used in several oceanic applications [21,35,36]. eCognition Developer 9.1 software [37] was used with the following multiresolution segmentation parameters: size 20, shape 0.5, and compactness 0.5.…”
Section: Geobia -Coastline Detectionmentioning
confidence: 99%
“…For this analysis, images were segmented into groups of pixels with similar spectral and spatial characteristics. A multiresolution segmentation algorithm that was previously used in several oceanic applications [21,35,36]. eCognition Developer 9.1 software [37] was used with the following multiresolution segmentation parameters: size 20, shape 0.5, and compactness 0.5.…”
Section: Geobia -Coastline Detectionmentioning
confidence: 99%
“…This results in dark regions or spots in satellite SAR images. Topouzelis et al, [21], emphasizes the importance of weathering processes, as they influence the oil spills physicochemical properties and detect-ability in SAR images. The processes that play the most important role for oil spill detection are evaporation, emulsifica- Crests at an angle ϕ to the look direction of the SAR [3].…”
Section: Oil Spill and Surface Films Impact On Bragg Scatteringmentioning
confidence: 99%
“…Therefore, Topouzelis et al, [21] and Topouzelis [22] reported that most studies use the low resolution of SAR data such as quick-looks, with the nominal spatial resolution of 100 m x 100 m, to detect oil spills. In this regard, quick looks' data are sufficient for monitoring large scale area of 300 km x 300 km.…”
Section: Oil Spill Detection In Sar Datamentioning
confidence: 99%
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“…Usually the oil spill candidates (that is the dark areas on SAR image) are identified with their geometric and radiometric parameters, then a classification algorithm is applied. In Topouzelis et al (2007) two different neural networks are used: one to detect dark spots on sea surface and another to classify the previously found areas as oil spills or look-alikes. The proposed method shows good results in detecting dark formations and discriminating oil spills from look-alikes as it detects with an overall accuracy of 94% the dark formations and discriminate correctly 89% of examined cases.…”
Section: Neural Network Approachmentioning
confidence: 99%