## 1 Introduction  
As one of the most significant sources of marine pollution, oil spills have caused serious environmental and economic impacts to the ocean and coastal zone [1]. Oil spills near the coast can be caused by ship accidents, explosion of oil rig platforms, broken pipelines, and deliberate discharge of tank-cleaning wastewater from ships. The NEEIDs program, sponsored by the European Commission, was the first robust attempt to use shipping, geological and metocean data to characterize oil spills in one of the major oil exploration areas of the world, prior to any major oil spill accident. Based on this data, oil spill models were established to simulate the development and trajectories of oil spills and investigate the susceptibility of coastal zone and find suitable measures to alleviate its impacts to the environment [2; 3; 4; 5]. Early warning and near-real-time monitoring of oil slicks plays a very important role in cleaning up operation of oil spill to alleviate its impact to coastal environment [2; 3]. Synthetic aperture radar (SAR) is one of most promising remote sensing systems for oil spill monitoring, for it can provide valuable information about the position and size of the oil spill [1]. Moreover, the wide coverage and all-day, all-weather capabilities make SAR very suitable for large scale oil spill monitoring and early warning [6; 7; 8]. In their early stages, studies of oil spill detection are mainly based on single polarimetric SAR images [9; 10; 11; 12]. The theoretical rationale of SAR oil spill detection is that the presence of oil slicks on the sea surface dampens short-gravity and capillary waves, so the Bragg scattering from the sea surface is largely weakened. The ideal sea surface wind speed for oil spills detection is 3-14 m/s [13]. As a result, oil spills can be detected as \"dark\" areas in SAR images. However, some other manmade or natural phenomena can result in very similar low scattering areas on the sea surface, e.g., biogenic slicks, waves, currents and low-wind areas, etc. Conventional oil spill detection procedures use intensity, morphological texture, and auxiliary information to distinguish mineral oil and its lookalikes, with its processing chain divided into three main steps [13]: (1) dark spot detection; (2) features extraction; and (3) classification between mineral and its lookalikes. Single polarimetric SAR-based oil spill detection algorithms need auxiliary information and large number of data samples to classify mineral oil and its lookalikes. Sometimes the shape and texture of oil slicks may vary, affecting the robustness of intensity-based oil spill classification algorithms. Polarimetric observation capabilities provided by advanced SAR sensors have much stronger capabilities for oil spills detection [14]. For instance, biogenic slicks and mineral oil are difficult to distinguish by single polarimetric SAR images. Yet, their polarimetric scattering mechanisms are largely different: for oil-covered areas, Bragg scattering is largely suppressed, and high polarimetric entropy can be documented. In the case of a biogenic slick, Bragg scattering is still dominant, but with a low intensity. Thus, similar polarimetric behaviors as those of oil-free areas should be expected in the presence of biogenic films. Hence, polarimetric features can largely help the image classification between mineral and biogenic lookalikes [14]. Various polarimetric features have been proposed to classify oil spills. The standard deviation of copolarized phase difference (phase difference between Vertical transmit and Vertical receive-VV and Horizonal transmit and Horizonal receive-HH channel) has shown a strong oil classification capability on C-, X-, and L-band data [15]. [PERSON] et al. (2011) proposed pedestal height to describe the different polarization signature between mineral oil and biogenic lookalikes [16]. [PERSON] et al. (2012) took the advantage of copolarization ratio to study the mixing status of crude oil and sea water [17]. [PERSON] et al.