title: [Classification of wetlands in multispectral remote sensing image based on HPSO and FCM].
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.
authors.affiliation: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. jwg76@163.com
authors.forename: Wei-Guo
authors.initials: WG
authors.lastname: Jiang
authors.forename: Qiang
authors.initials: Q
authors.lastname: Chen
authors.forename: Ji
authors.initials: J
authors.lastname: Guo
authors.forename: Hong
authors.initials: H
authors.lastname: Tang
authors.forename: Xue
authors.initials: X
authors.lastname: Li
mesh_terms: D025341:Principal Component Analysis; D058998:Remote Sensing Technology; D013686:Telemetry; D053833:Wetlands