摘要
主要研究了基于粗糙集和支持向量机的模式分类技术,结合遥感图像中军用飞机目标识别进行了理论分析和实验研究。设计了基于粗糙集和决策有向无环图的支持向量机模式识别分类器,对分类的性能进行了分析研究,利用粗糙集理论中属性约简方法去除冗余属性,降低飞机特征维数,提出使用决策有向无环图支持向量机进行训练并对待识别目标预测。实验表明,本文提出的方法可提高识别速度及预测分类精度。
In this paper, technique of pattern recognition based on rough set and support vector machine combined with the airplane recognition for optical remote sensing images is analyzed and researched. A pattern recognition classifier based on the advantage of rough set and DAGSVM （Directed Acyclic Graph Support Vector Machines） is designed in this paper. Employing heuristic method of reduction induce the classification rule from decision table and DAGSVM method in a remote sensing classification experiment. As a result, this method provides a high recognition precision and speed with high capability of anti-disturbance to improve the performances of classifier effectively. By means of comparable research and result analysis of classifier, some satisfied research fruit is obtained.
出处
《微计算机信息》
2009年第31期8-9,17,共3页
Control & Automation
基金
基金申请人:郭雷
项目名称:交互式语义记忆学习的图像检索模型
基金颁发部门:国家自然科学基金委(60675015)
关键词
目标识别
不变矩
复数矩
粗糙集
支持向量机
遥感图像
automatic target recognition
invariant moment
complex number moment
rough sets
support vector machine
remote sensing imagery
