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

• Main Authors: WU Shihua, WU Yiquan, ZHOU Jianjiang, MENG Tianliang
• Format: Article
• Language: zho
• Published: Surveying and Mapping Press 2015-11-01
• Series: Acta Geodaetica et Cartographica Sinica
• Subjects: river detection; synthetic aperture radar image segmentation; multi-threshold selection; reciprocal gray entropy; artificial bee colony optimization; Chan-Vese(CV)model
• Online Access: http://html.rhhz.net/CHXB/html/2015-11-1255.htm

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 Dirac function is replaced with the image edge intensity and the coarse segmentation results serve as the initial condition of improved CV model which is utilized 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 well as segmentation accuracy.