Marine spill oil SAR image segmentation based on Tsallis entropyand improved Chan Vese model
Tsallis熵和改进CV模型的海面溢油SAR图像分割

Considering the low accuracy of Synthetic Aperture Radar(SAR)image segmentation in the marine spill oil detection,a segmentation method of marine spill oil images based on Tsallis entropy multilevel thresholding and improved Chan Vese(CV)model is proposed in this paper. First, the multi-threshold selection algorithm based on Tsallis entropy is used to make a coarsesegmentation for marine spill oil images. The obtained spill oil region and its coarse contour provide local region and initial contourfor CV model, respectively, which are used to reduce the scene complexity of CV model and its sensitivity to initial situation.The traditional CV model only considers the mean value of each region of image instead of the local information of image. Thoughit can get non-gradient def ined image boundary, there are errors in the segmented results. We use an improved CV model with themotion factor, thus the segmentation errors are reduced and the convergence speed is increased. Experimental results show that theour method not only dispenses with initial condition, but also ensures accurate segmentation boundary and eff icient operation.