The Gaussian beam focusing was employed as the main method of characterizati
From the time of its inception, Nutlin-3a IC50 Synthetic Aperture Radar (SAR) imaging systems have provided a remote sensing resource complementary to optical and thermal-infrared sensors. SAR Imagery has been applied in a gamut of areas including ecological observation, surface monitoring, target detection, and mapping. Primary among the advantages of SAR imaging are day-night and all-weather operation, wide area coverage and sensor height-independent image resolution. These functionalities have also led to an escalating interest in the last decade towards the usage of SAR imagery for ocean environment monitoring.Nevertheless, the multiplicative speckle noise caused by microwave illumination degrades the quality of the SAR imagery. Speckle impedes visual interpretation of SAR images and may lead to interclass confusion. This is particularly so in the quick detection of oil slicks on the sea surface. The low backscatter cross section of the surface of oil spillages causes Bragg wave dampening effects on the sea. As a result of this an oil spill comes out on the image as a dark slick or dark spot, whereas the surrounding water appears bright. Consequently, oil slicks in SAR images are characterized by a high noise and low contrast, disturbing their extraction and analysis.A variety of methods have been developed to reduce speckles. For example, geometric filters such as those of Frost [1] and Lee [2] have been designed. However, convolving the image with filters could reduce the segmentation accuracy by blurring the feature boundaries due to high noise [3]. Traditional edge segmentation methods, such as border tracing, have drawbacks as well. In these methods, use of a small convolution window to preserve image resolution generates an edge map with incomplete feature boundaries and spurious edge points, due to high noise. In recent years, several novel methods have been attempted for reducing speckles while preserving edges such as Markov Random Fields [4][5] and wavelet methods [6]. Unlike these methods, level set provides a new option that is able to remove high noise and simultaneously delineate a smooth and stable boundary [7].Level set was proposed by Osher and Sethian [8] as an interface propagation technique for various applications including image segmentation. In this method, a curve is embedded as a zero level set of a higher dimensional surface. Following that, the entire surface is evolved to minimize a metric defined by the curvature and image gradient, i.e. the speed terms of the level set will reduce to zero when reaching the object boundary. The level set interface can propagate with topological changes, significant protrusion and narrow regions.In the traditional level set technique, instead of tracking the points on the interface itself, the interface is embedded as the zero level set propagates by iterations.