摘要
基于扩展的高斯混合密度降解模型,本文提出一种利用遗传算法实现逐步寻优的特征挖掘模型。利用该模型可以从混合密度特征空间中挖掘出多个未知特征并以树状的层次方式逐步分离出来。为了更好地与实际情况相吻合,本文引入符号化的知识处理方法,提出了基于知识的空间特征逐步寻优挖掘模型,并将其应用到遥感影像的实例分析,取得了较好的效果。
Extending the method of Gaussian mixture modeling and decomposition （ GMDD ） , a new feature mining method named stepwise optimization model（SOMM） with Genetic Algorithms （GA） is proposed in this paper. This method is used in the extraction of tree-like hierarchical structure of unknown feature distributions in feature space. To approximate reality accurately, integration of SOMM-GA with symbolic geographical knowledge is essential in the feature mining and classification of remote sensing images. A knowledge-integrated SOMM-GA model that combines the power of SOMM-GA and logic reasoning of rule-based inference is proposed. In addition to conceptual and technical discussions of the model in detail, it is tested in a practical application on some district.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2005年第6期735-741,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.40101021)
中科院地理科学
资源研究所知识创新(No.CXIOG-D02-01)资助项目
关键词
空间逐步寻优
特征挖掘
遗传算法
知识
影像分类
Stepwise Optimization Modal, Feature Mining, Genetical Algorithm, Knowledge,Image Classification
