A fast,edge-preserving,distance-regularized model with bilateral fi ltering for oil spill segmentation of SAR images

Marine oil spills are among the most signifi cant sources of marine pollution. Synthetic aperture radar(SAR) has been used to improve oil spill observations because of its advantages in oil spill detection and identifi cation. However, speckle noise, weak boundaries, and intensity inhomogeneity often exist in the oil spill regions of SAR imagery, which will seriously aff ect the accurate identifi cation of oil spills. To enhance marine oil spill segmentation of SAR images, a fast, edge-preserving framework based on the distance-regularized level set evolution(DRLSE) model was proposed. Specifi cally, a bilateral fi lter penalty term is designed and incorporated into the DRLSE energy function(BF-DRLSE) to preserve the edges of oil spills, and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity. Two sets of RadarSat-2 SAR data were used to test the proposed method. The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution. Compared with other methods, the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy(97.83%). In addition, using an appropriate initial box boundary for the DRLSE method accelerated the global search process, improved the accuracy of oil spill segmentation, and reduced computational time. Therefore, the results suggest that the proposed framework is eff ective and applicable for marine oil spill segmentation.