2015
DOI: 10.1080/01431161.2015.1057301
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SAR polarimetry for sea oil slick observation

Abstract: In this article, a review of polarimetric synthetic aperture radar (SAR) methods for sea oil slick observation is presented. Marine oil pollution monitoring is a topic of great applicative and scientific relevance. In this framework, the use of remotely sensed measurements is of special interest and, in particular, the SAR because of its almost all-weather and all-day imaging capability at fine spatial resolution is the most effective tool. Conventional single-polarization SAR oil spill monitoring techniques a… Show more

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Cited by 132 publications
(87 citation statements)
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References 63 publications
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“…On the contrary, the values of ν, µ, p and M33 of oil spill are low, therefore their image tone should be darker than the area covered by water or lookalike. According to this criterion [26], in terms of behavior of fully Pol-SAR features of an oil spill and water/lookalike, we can determine that the long strip objects in Dataset 1 and Dataset 2 in Figure 2, are oil spills, rather than lookalikes. Table 1 is a sectional table extracted from Reference [26], summarizing the behaviors of fully Pol-SAR features of seawater, lookalike and oil spill.…”
Section: Remote Sensing Datamentioning
confidence: 99%
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“…On the contrary, the values of ν, µ, p and M33 of oil spill are low, therefore their image tone should be darker than the area covered by water or lookalike. According to this criterion [26], in terms of behavior of fully Pol-SAR features of an oil spill and water/lookalike, we can determine that the long strip objects in Dataset 1 and Dataset 2 in Figure 2, are oil spills, rather than lookalikes. Table 1 is a sectional table extracted from Reference [26], summarizing the behaviors of fully Pol-SAR features of seawater, lookalike and oil spill.…”
Section: Remote Sensing Datamentioning
confidence: 99%
“…According to this criterion [26], in terms of behavior of fully Pol-SAR features of an oil spill and water/lookalike, we can determine that the long strip objects in Dataset 1 and Dataset 2 in Figure 2, are oil spills, rather than lookalikes. Table 1 is a sectional table extracted from Reference [26], summarizing the behaviors of fully Pol-SAR features of seawater, lookalike and oil spill. According to the table, it can be known the pixel values of  and H of oil spills are high, so their image tone should be brighter than the area covered by water or lookalike.…”
Section: Remote Sensing Datamentioning
confidence: 99%
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“…K-means is an unsupervised clustering method, which divides data into clusters that are as compact as possible [Theodoridis and Koutroumbas, 2006]. K-means clustering has also been used widely in other applications including oil spill detection [Migliaccio et al, 2015;Wang et al, 2014;Ganesan, 2015] and sea ice classification [Gill et al, 2013;Karvonen, 2010;Korosov et al, 2015] and is therefore a well-developed approach for classification applications using remote sensing data. We have deliberately chosen to implement an unsupervised classification procedure since it is our ultimate goal to develop a fully automated, operational system for avalanche debris detection that does not require additional human input.…”
Section: Earth and Space Sciencementioning
confidence: 99%
“…This can be exploited as one case to sort out most of the look-alikes that are typical, such as biogenic films (slicks that are produced by marine organisms, such as fishes, algae, etc. ), which normally cause very little harm to the marine environment [12,13].…”
Section: Introductionmentioning
confidence: 99%