摘要
高斯混合密度降解模型 ( GMDD)是一种基于稳健统计理论的层次聚类方法。GMDD的分布模型是假设特征空间是由一组混合的高斯 ( Gaussian)分布组成的 ,然后通过一定优化算法来获得特征空间中与预先假设最符的特征分布 ,并逐步分离出特征空间 ,直到特征空间全部降解为一组特征模式的混合密度分布集。GMDD与传统的统计聚类相比较 ,主要优点有 :特征类别不受限定、抗干扰力强、参数估计与初始无关、考虑密度分布的可变性等。初步探讨了基于 GMDD方法的遥感影像特征估计模型和方法 ( GIFEM) ,并提出基于遗传算法的 GMDD优化模型。
Gaussian Mixture Density Modelling and Decomposition (GMDD) is a hierarchical clustering method based on robust statistical theory. Firstly, GMDD is assumed with a mixture group of Gaussian distribution in feature space, then by optimization algorithm the feature which mostly accord with the assumed distribution is hierarchically extracted from space until all of the features in the space are decomposed to a group of featuring pattern. Compared with conventional statistical clustering methods, GMDD's main outstanding superorities are:(1)Initial number of features does not need to be specified a priori; (2)The proportion of noisy data in the mixture can be large; (3)The parameters estimation of each feature is virtually initial independent; and (4)The variability in the shape and size of the feature densities in the mixture is taken into account. The article presented the model named the GMDD based remote sensing image feature estimation model (GIFEM) , and the model of GA space searching optimization is also presented out.
出处
《遥感技术与应用》
CSCD
2000年第1期45-50,共6页
Remote Sensing Technology and Application
关键词
影像特征
遥感
稳健统计理论
GIS
Robust statistics, Gaussian mixture density, Image features
