An Efficient Marine Oil Spillage Identification Scheme Based on an Improved Active Contour Model Using Fully Polarimetric SAR Imagery

Abstract

Oil spillage pollution results in damages to marine and coastal environments and ecosystems at all levels. Due to the distinct advantages of fully polarimetric synthetic aperture radar (Pol-SAR), it has been widely employed to improve oil spillage event observations. In this paper, with the aim of enhancing ocean oil spillage identification, an improved scheme was proposed, which was mainly based on an active contour model (ACM), with a special emphases on choosing an optimal initial box boundary for the ACM model, and a noise suppressing process. In this proposed scheme, the ACM initial box boundary was chosen to be the median level contour of the imagined mountain if regarding image grey values as elevations. Meanwhile, following Lee filter operator, a Gaussian smooth operator was hired to further weaken the speckle noise, thereby making the evolution of ACM more stable. Three sets of RADARSAT-2 fully Pol-SAR data were used to test of the proposed method. In addition, the current study achieved the first complete comparison of the identification abilities of the ten most common recently used fully Pol-SAR features for oil spills. The experimental results show: 1) The features of H; v; span; P; ${\text{M}}_{33}$ ; and $\rho $ possessed good distinguishable abilities for oil spillage detection in sea water. Specially, features span and v outperformed other Pol-SAR features with regard to the elaborate distinguishing identifications of small patches of oil spill objects from other small objects, such as oil platforms or ships; 2) An appropriate setting of the initial box boundary of the ACM was able to accelerate the global search, and eventually led to improvements in the accuracy of the oil spill identification, while simultaneously reducing the computational time; 3) The effective noise suppression was confirmed to be very necessary, guaranteeing stability of the ACM evolution. When compared with other traditional methods, the proposed improved scheme was found to be more effective in the overall identification accuracy (99.8%).