1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China 2. Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China 3. School of Mathematical Science, Beijing Normal University, Beijing 100875, China
Abstract：The present paper analyzed the characteristics of particle swarm optimization(PSO), hybrid particle swarm optimization (HPSO) and fuzzy C-means (FCM), imported FCM into HPSO, and improved the HPSO-FCM arithmetic. An HPSO-FCM program was developed using Fortran language in MATLAB. Besides, a synthesis image combined with the former three principal components was obtained through band stacking and principal component analysis, taking the multispectral visible image of HJ-1 Satellite shot in June 2009 and the ASAR radar image of ENVISAT as basic data. And the paper has done a wetlands classification experiment in the synthesis image of the East Dongting Lake of Hunan province, using HPSO-FCM arithmetic and ISODATA separately. The results indicated: (1) The arithmetic which imported crossover operator of genetic algorithms and FCM into HPSO had better search speed and convergent precision, and it could search and optimize the best cluster center more efficiently. (2) The HPSO-FCM arithmetic has better precision in wetlands classification in multispectral remote sensing image, and it is an effective method in remote sensing image classification.
[1] Paul A Keddy. Wetland Ecology Printiples and Consercation. Cambridge: Cambridge University Press, 2000. 124. [2] Holland M M. Wetlands and Environment Gradients. In: M ulamoottil G, W arner B G, McBean E A. Wetland Environment Gradients, Boundaries and Buffers. CRC Press Inc. 1996. 112. [3] Johnson R M, Barson M M. Australian Journal of Marine and Freshwater Research, 1993, 44(2): 235. [4] Wright C, Gallant A. Remote Sensing of Environment, 2007, 107(4): 582. [5] Li Xue, Jiang Weiguo, Sheng Shaoxue, et al. Research on Wetland Classification Approaches Based on Hyperion Hyperspectral Image. Proceedings of SPIE, 2009, 7498, 74981O. 1. [6] Eberhart R C, Kenedy J A. Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, 1995. 1942. [7] Dunn J C. Cybernetics and Systems, 1973, (3): 32. [8] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981. [9] CHEN Xi, LI Chun-yue, LI Feng, et al(陈 曦, 李春月, 李 峰, 等). Computer Engineering and Application(计算机工程与应用), 2008, 44(18): 181. [10] CHEN Rong-yuan, ZHANG Fei-yan, ZHANG Bin, et al(陈荣元, 张飞艳, 张 斌, 等). Journal of Electronics & Information Technology(电子与信息学报), 2009, 31(10): 2509. [11] ZHANG Lei, GAO Shang(张 磊, 高 尚). Computer Application and Seftware(计算机应用与软件), 2009, 26(12):89. [12] JI Zhen, LIAO Hui-lian, WU Qin-hua(纪 震, 廖惠连, 吴青华). The Application and Algorithm of Particle Swarm Optimization(粒子群算法及应用). Beijing: Science Press(北京: 科学出版社), 2009. [13] Angeline P. Using Selection to Improve Particle Swarm Optimization. In: Proceedings of IEEE Intl.Conf. on Evolutionary Computation(ICEC’98), Anchorage, 1998. 84. [14] Lovbjerg M. Rasmussen.Hybrid Particle Swarm Optimization with Breeding and Subpopulations. In: Proceedings of the third Genetic and Evolutionary Computation Conference, 2001. 56. [15] DUN Xiao-dong, WANG Cun-rui, LIU Xiang-dong(段晓东, 王存睿, 刘向东). The Application of Particle Swarm Optimization(粒子群算法及应用). Liaoning: Liaoning University Press(辽宁: 辽宁大学出版社), 2007. [16] YANG Kai, JIANG Hua-wei(杨 凯, 蒋华伟). Computer Engineering and Application(计算机工程与应用), 2009, 45(33): 179.
