基于量子粒子群算法选择特征的遥感图像分类  

Remote Sensing Image Classification Based on Selecting Features of Quantum Particle Swarm Optimization Algorithm

作　　者：莫才健[1] 武锋强[1] 陈莉[1] 邹强[1] 

机构地区：[1]西南科技大学环境与资源学院,四川绵阳621010

出　　处：《吉林大学学报（理学版）》2018年第2期368-374,共7页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金重点基金(批准号:41401598);四川旅游发展研究中心基金(批准号:LYM12-11);西南科技大学博士基金(批准号:14zx7128)

摘　　要：针对目前遥感图像分类算法存在精度低、速度慢等问题,提出一种基于量子粒子群算法的遥感图像分类算法,以提高遥感图像的分类效果.首先分析目前遥感图像分类算法存在的不足及其原因;然后提取多种类型的遥感图像原始特征,采用量子粒子群算法对特征进行筛选,以提取对遥感图像分类结果较重要的特征;最后采用最小二乘支持向量机(LSSVM)建立遥感图像分类器,实现遥感图像分类和识别,并进行遥感图像分类的仿真对比实验.实验结果表明,该算法克服了当前遥感图像分类算法存在的局限性,大幅度提高了遥感图像的分类精度,有效减少了图像分类误差,提高了图像分类效率.In view of the shortcomings of the low accuracy and slow speed of current remote sensing image classification algorithm,we proposed a remote sensing image classification algorithm based on quantum particle swarm optimization to improve the classification effect of remote sensing images.Firstly,we analyzed the shortcomings of the current remote sensing image classification algorithm and its reasons.Secondly,we extracted the original features of various types of remote sensing images,and used the quantum particle swarm algorithm to select features and extract important features of remote sensing image classification results.Finally,using least squares support vector machine,we established classifier for remote sensing image,realized remote sensing image classification and recognition,and carried out simulation and contrast experiment of remote sensing image classification.The experimental results show that the proposed algorithm overcomes the limitations of current remote sensing image classification algorithms,and the classification accuracy of remote sensing image has been greatly improved,which effectively reduces the error of image classification and improves the efficiency of image classification.

关 键 词：遥感技术 粒子群优化算法 分类器设计 特征提取 量子行为 

分 类 号：TP381[自动化与计算机技术—计算机系统结构]