有限混合模型FM的分级聚类已广泛应用于不同领域,然而,它的计算复杂度与观测数据的平方成正比,因此,在海量数据方面的应用就受到了限制。另一方面,多光谱图像数据中同时包含有空间和光谱两类信息,但大多数基于像素的多光谱图像聚类方法,仅使用了其频谱信息而忽视了空间信息。本文提出了一种新的基于广义有限混合模型GFM的分级聚类方法,该算法把MRF随机场和GFM模型结合在一起,分类数可以通过PLIC准则自动确定。算法在执行过程中,采用K均值聚类方式获得过分类图像,分级聚类从过分类图像开始,代替原来从单点类开始的方式,这样可以方便获取GFM模型成分密度的初始参数。最后,采用由Gibbs采样器生成的仿真测试图对算法的精度进行了定量评价,通过与K均值聚类和FM聚类的比较说明了本文算法的优越性,同时用荷兰Flevoland农业地区的极化SAR图像验证了本文算法的有效性。

Hierarchical clustering based on the finite mixture model(FM) has shown very good performance in a number of fields.However,it generally requires storage and computing at least proportional to the square of the dimension of observations,so that its application to large datasets has been hindered by time and memory complexity.Another,multispectral images provide detailed data with information in both the spatial and spectral domains.But many clustering methods for multispectral images are based on a per-pixel classification,while uses only spectral information and ignores spatial information.In this work,a new hierarchical clustering based on GFM model,suitable for large datasets,e.g.,multispectral remote sensing images,is proposed.This algorithm integrates with GFM model with Markov random field.The number of clusters is automatically identified by using the pseudolikelihood information criterion(PLIC).An oversegmented image is obtained by a simple K-means clustering method.Instead of starting with singleton clusters,hierarchical clustering is applied on the oversegmented image.Initial parameters of component densities of GFM model can be easily extracted.At last,the accuracy of the algorithm is quantitatively evaluated through simulated test image generated by using Gibbs sampler.The experiment show a superior performance compared to several other methods,such as K-means and classical hierarchical clustering based on the classical FM model.Its validity is also illustrated by using a polarimetric SAR image of Flevoland in the Netherlands.