基于细菌觅食算法的SAR图像阈值分割

doi:10.3969/j.issn.1001-2400.2011.06.025

Abstract

Abstract:

In order to increase the speed and the accuracy of Synthetic Aperture Radar(SAR) image segmentation, a new thresholding segmentation method is proposed, which integrates the Bacterial Foraging Algorithm (BFA) and two-dimensional grey entropy. After the basic BFA is analyzed deeply, the method improves the search speed of the best threshold via shrinking the foraging space of the bacterial swarm. And then, an improved two-dimensional grey entropy model is regarded as the fitness function of our optimized BFA. Finally, the best threshold is located gradually and quickly by three behaviors of the bacterial swarm, i.e., chemotaxis, and reproduction, elimination and dispersal. Experimental results show that the proposed method is superior to some segmentation methods based on the Genetic Algorithm (GA) and Artificial Fish Swarm Algorithm (AFSA) in convergence, stability and segmentation effects.

Key words: image segmentation, bacterial foraging algorithm, global optimization, SAR image

CLC Number:

TP391.41，N941.5

MA Miao;LIANG Jianhui;GUO Min. SAR image thresholding segmentation based on the bacteria foraging algorithm[J].J4, 2011, 38(6): 152-158+178.

References

［1］梁艳春, 吴春国, 时小虎, 等. 群体智能优化算法理论与应用［M］. 北京: 科学出版社, 2009.
［2］ Wang Xingyuan, Li Fanping, Wang Shuguo. Fractal Image Compression Based on Spatial Correlation and Hybrid Genetic Algorithm［J］. Journal of Visual Communication and Image Representation, 2009, 20(8): 505-510.
［3］ Cerello P, Cheran S C, Bagnasco S, et al. 3-D Object Segmentation Using Ant Colonies ［J］. Pattern Recognition, 2010, 43(4): 1476-1490.
［4］陈果, 左洪福. 图像分割的二维最大熵遗传算法［J］. 计算机辅助设计与图形学学报, 2002, 14(6): 530-534.
Chen Guo, Zuo Hongfu. 2-D Maximum Entropy Method of Image Segmentation Based on Genetic Algorithm ［J］. Journal of Computer-aided Design ＆Computer Graphics, 2002, 14(6): 530-534.
［5］马苗, 鹿艳晶, 张艳宁, 等. SAR图像的二维灰熵模型快速分割方法［J］. 西安电子科技大学学报, 2009, 36(6): 1114-1119.
Ma Miao, Lu Yanjing, Zhang Yanning, et al. Fast SAR Image Segmentation Method Based on the Two-dimensional Grey Entropy Model ［J］. Journal of Xidian University, 2009, 36(6): 1114-1119.
［6］过润秋, 李俊峰, 林晓春. 基于并行遗传算法的红外图像增强及相关技术［J］. 西安电子科技大学学报, 2004, 31(1): 6-8.
Guo Runqiu, Li Junfeng, Lin Xiaochun. The Infrared Image Enhancement and the Correlative Technique Based on the Parallel Genetic Algorithm［J］. Journal of Xidian University, 2004, 31(1): 6-8.
［7］ Raghavendra R, Dorizzi B, Rao A, et al. Particle Swarm Optimization Based Fusion of Near Infrared and Visible Images for Improved Face Verification［J］. Pattern Recognition, 2011, 44(2): 401-411.
［8］ Du Feng, Shi Wenkang, Chen Liangzhou, et al. Infrared Image Segmentation with 2-D Maximum Entropy Method Based on Particle Swarm Optimization［J］. Pattern Recognition Letters, 2005, 26(5): 597-603.
［9］李晓磊, 邵之江, 钱积新. 一种基于动物自治体的寻优模式:鱼群算法［J］. 系统工程理论与实践, 2002, 22(11): 32-38.
Li Xiaolei, Shao Zhijiang, Qian Jixin. An Optimizing Method Based on Autonomous Animates: Fish-swarm Algorithm ［J］. System Engineering-Theory ＆ Pratice, 2002, 22(11): 32-38.
［10］ Karaboga D. An Idea Based on Bee Swarm for Numerical Optimization ［R/OL］. ［2011-04-28］. http://mf.erciyes.edu.tr/abc/pub/tr06-2005.pdf.
［11］ Passino K M. Biomimicry of Bacterial Foraging for Distributed Optimization and Control ［J］. IEEE Control System Magazine, 2002(6): 52-67.
［12］范玉军, 王冬冬, 孙明明. 改进的人工鱼群算法［J］. 重庆师范大学学报(自然科学版), 2007, 24(3): 23-26.
Fan Yujun, Wang Dongdong, Sun Mingming. Improved Artificial Fish-school Algorithm［J］. Journal of Chongqing Normal University (Natural Science Edition), 2007, 24(3): 23-26.
［13］刘思峰, 党耀国, 方志耕. 灰色系统理论及其应用［M］. 北京: 科学出版社, 2004.
［14］周德龙, 潘泉, 张洪才, 等. 最大熵阈值处理算法［J］. 软件学报, 2001, 12(9): 1420-1422.
Zhou Delong, Pan Quan, Zhang Hongcai. Maximum Entropy Thresholding Algorithm［J］. Journal of Software, 2001,12 (9): 1420-1422.
［15］潘喆, 吴一全. 二维Otsu图像分割的人工鱼群算法［J］. 光学学报, 2009, 29(8): 2115-2121.
Pan Zhe, Wu Yiquan. The Two-dimensional Otsu Thresholding Based on Fish-swarm Algorithm ［J］. Acta Optica Sinica, 2009, 29(8): 2115-2121.

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SAR image thresholding segmentation based on the bacteria foraging algorithm

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