一种融合PSO和Isodata的遥感图像分割新方法  

A New Method of Remote Sensing Image Segmentation Based on PSO and Isodata

作　　者：马彩虹[1,2] 戴芹[1] 刘士彬[1] 

机构地区：[1]中国科学院对地观测与数字地球科学中心,北京市海淀区邓庄南路9号100086 [2]中国科学院研究生院,北京市玉泉路甲19号100049

出　　处：《武汉大学学报（信息科学版）》2012年第1期35-38,共4页Geomatics and Information Science of Wuhan University

基　　金：中国科学院对地观测与数字地球科学中心主任科学基金资助项目;国家自然科学基金资助项目(40701105)

摘　　要：针对当前遥感图像分割方法存在的缺点,将人工智能领域的粒子群优化方法应用到遥感图像分割方面,提出了一种融合PSO和Isodata的遥感图像分割新方法。对不同分辨率遥感图像的分割实验结果表明,融合PSO和Isodata的遥感图像分割新方法能够自适应确定聚类数目,避免了聚类过程的随机性,使分割结果更加接近实际情况。In order to avoid disadvantages of the current algorithms,the Particle Swarm Optimization（PSO） algorithm in the field of artificial intelligence is successfully applied to remote sensing image segmentation.And a new algorithm combined PSO and Isodata is proposed in this paper.This method first changes the color space of the images,then the initial cluster number are determined by the combined algorithm.Finally,the automatic segmentation of remote sensing images is achieved through multiple iterations.Through many experiments of remote sensing images with different spatial resolution,the results show that the new algorithm can determine the initial cluster number adaptively,avoid the local optima of K-means and Isodata algorithms,increase the searching capability of PSO,and the segmentation results are much more close to the actual situation.So it is a new effective algorithm for the segmentation of remote sensing images.

关 键 词：PSO算法 ISODATA算法 特征距离 遥感图像分割 

分 类 号：P237.3[天文地球—摄影测量与遥感]