Classification of Wetlands in Multispectral Remote Sensing Image Based on HPSO and FCM

被引：1
|
作者：
Jiang Wei-guo [1 ,2 ]
Chen Qiang [1 ,2 ]
Guo Ji [3 ]
Tang Hong [1 ,2 ]
Li Xue [1 ,2 ]
机构：
[1] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, Key Lab Environm Change & Nat Disaster, Beijing 100875, Peoples R China
[3] Beijing Normal Univ, Sch Math Sci, Beijing 100875, Peoples R China
来源：
SPECTROSCOPY AND SPECTRAL ANALYSIS | 2010年 / 30卷 / 12期
关键词：
Hybrid particle swarm optimization; Fuzzy C-means; Wetland classification; Remote sensing;
D O I：
10.3964/j.issn.1000-0593(2010)12-3329-05
中图分类号：
O433 [光谱学];
学科分类号：
0703 ; 070302 ;
摘要：
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.
引用
收藏
页码：3329 / 3333
页数：5
相关论文
共 16 条
  • [11] Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
  • [12] LI X, 2009, P SOC PHOTO-OPT INS, P7498
  • [13] LOVBJERG M, 2001, P 3 GEN EV COMP C, P56
  • [14] Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data
    Wright, Chris
    Gallant, Alisa
    [J]. REMOTE SENSING OF ENVIRONMENT, 2007, 107 (04) : 582 - 605
  • [15] Yang Kai, 2009, Computer Engineering and Applications, V45, P179, DOI 10.3778/j.issn.1002-8331.2009.33.058
  • [16] Zhang Lei, 2009, COMPUTER APPL SEFTWA, V26, P89
12