中文摘要：

在多光谱的图象分类的一个一般他们(期望最大化) 算法的使用被知道引起二个问题：变化协变性矩阵和随机选择的起始的价值的敏感的奇特。以前的原因计算失败；后者生产不稳定的分类结果。这份报纸建议一条修改途径解决这些缺点。首先，修正被建议基于一个 k 工具算法为 EM 算法决定可靠参数，起始的中心从第一个主要部件的密度函数获得了，它在随机避免起始的中心的选择。第二修正使用图象的主要部件转变获得一套 uncorrelated 数据。EM 算法的输入被主要贡献率决定的主要部件的数字。这样，修正不能仅仅移开奇特而且削弱噪音。从二个不同传感器获得的遥感图象的二个集合获得的试验性的结果证实建议途径的有效性。

英文摘要：

The use of a general EM （expectation-maximization） algorithm in multi-spectral image classification is known to cause two problems： singularity of the variance-covariance matrix and sensitivity of randomly selected initial values. The former causes computation failure; the latter produces unstable classification results. This paper proposes a modified approach to resolve these defects. First, a modification is proposed to determine reliable parameters for the EM algorithm based on a k-means algorithm with initial centers obtained from the density function of the first principal component, which avoids the selection of initial centers at random. A second modification uses the principal component transformation of the image to obtain a set of uncorrelated data. The number of principal components as the input of the EM algorithm is determined by the principal contribution rate. In this way, the modification can not only remove singularity but also weaken noise. Experimental results obtained from two sets of remote sensing images acquired by two different sensors confirm the validity of the proposed approach.