利用倒数灰度熵和改进Chan-Vese模型进行SAR河流图像分割

为了进一步提高合成孔径雷达(SAR)图像中河流分割的精度和速度,提出了一种基于人工蜂群优化的倒数灰度熵多阈值选取与改进Chan-Vese(CV)模型相结合的分割方法。考虑SAR图像中河流目标和背景类内灰度的均匀性,提出了基于蜂群优化的倒数灰度熵多阈值选取方法,以此对河流图像进行粗分割;针对基本CV模型收敛速度低、对初始条件敏感的问题,利用图像边缘强度取代Dirac函数,将粗分割结果作为改进CV模型的初始条件,对河流图像进行细分割。大量试验结果表明,所提出的分割方法无须设置初始条件,运行速度快,分割精度高。

SAR River Image Segmentation Based on Reciprocal Gray Entropy and Improved Chan-Vese Model

To further improve the accuracy and speed of river segmentation on synthetic aperture radar (SAR)images,a segmentation method is proposed,which is based on improved Chan-Vese (CV)model combining with reciprocal gray entropy multi-threshold selection optimized by artificial bee colony algorithm.Considering the uniformity of the gray level within river object cluster and background cluster,a coarse river image segmentation is made by using the multi-threshold selection algorithm based on reciprocal gray entropy and artificial bee colony optimization;Contrapose the low convergence speed and the sensitivity to initial conditions of basic CV model,the Di rac function is replaced with the image edge intensity and the coarse segmentation results serve as the initial condition of improved CV model which is uti l ized to make a fine segmentation for the river image.A large number of experimental results show that, the proposed segmentation method needs not set initial conditions and has high running speed as wel l as segmentation accuracy.