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 are limited in their capability to detect oil slicks since they strongly rely on suitable thresholds, training samples, and ancillary information. Hence, an expert image analyst is due. The launch of a number of polarimetric SAR missions, along with the understanding of the peculiar physical mechanisms governing the scattering by an oil slick, led to a new paradigm (known as physical processing) that fostered a set of polarimetric algorithms particularly robust and efficient. Hence, suitable polarimetric models that exploit the departure from the slick-free sea Bragg scattering have been developed to effectively address oil slick monitoring. A set of polarimetric features extracted following such electromagnetic models have been proved to be reliable for oil slick monitoring. Polarimetric SAR observations led to a significant improvement in sea oil slick observation since they allow distinguishing oil slicks from a broad class of lookalikes in an unsupervised way. In addition, deeper information on the damping properties of the pollutant can be also inferred, which is of paramount importance for remediation purposes. Such physical processing has been demonstrated to be robust at variance of microwave carrier frequency, e.g. L-, C-, and X-band, and suitable to be exploited to extract information by dual-polarized, full-polarized, and compact modes. All these make such a physical approach of operational interest since it is able to exploit a larger set of SAR measurements building up a virtual constellation. In this review all these are detailed.

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

“…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.…”

“…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.…”

“…Both images are acquired in fine quad-polarization imaging in the Gulf of Mexico. The RADARSAT-2 fine quad-polarization imaging mode provides single-look complex data in HH, VV, HV, and VH channels with a low noise floor of 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 α and H of oil spills are high, so their image tone should be brighter than the area covered by water or lookalike.…”

A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

“…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.…”

Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

“…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].…”