遥感图像最大似然分类方法的EM改进算法

基于参数化密度分布模型的最大似然方法（MLC）是遥感影像分类最常用手段之一，与其他非参数方法（如神经网络）相比较，它具有清晰的参数解释能力、易于与先验知识融合和算法简单而易于实施等优点。但是由于遥感信息的统计分布具有高度的复杂性和随机性，当特征空间中类别的分布比较离散而导致不能服从预先假设的分布，或者样本的选取不具有代表性，往往得到的分类结果会偏离实际情况。首先介绍了用基于有限混合密度理论的期望最大（EM）算法来作为最大似然函数（MLC）参数估计的方法－EM－MLC。该模型首先假设总体混合密度分布可被分解为有限个参数化的高斯密度分布，然后把具有先验知识的样本与随机选取的未知样本混合在一起，通过EM迭代计算来估计出各密度分布的最大似然函数的参数集，从而一定程度上避免了参数估计可能出现的偏离。最后，本文提出了基于EM－MLC遥感影像分类的具体实施流程和应用示范，并与一般最大似然方法（MLC）得到的分类结果进行了定性和定量的综合比较，认为EM－MLC在精度上得到了提高。

The EM-based Maximum Likelihood Classifier for Remotely Sensed Data

Based on parametric density distribution model, the maximum likelihood classification (MLC) might be one of the most popular methods for remote sensing image classification. By comparison with non parametric approaches, MLC has several distinct advantages, such as its clear parametric interpretability, feasible integration with prior knowledge based on Bayesian theory, and relative simple realization, etc. However, remote sensing information has some degree of definite statistical characteristic, but as well as holds the high randomness and complexity, which generally behaves as mixture density distribution in feature space. If the distributions of certain categories in feature space are so discrete that they might not obey to the single assumed distribution, or the training samples are not sufficient enough that they can not represent the overall distributions, it often brings on great bias between obtained results and practical situations. In this article, we firstly introduce into the expectation maximization (EM) algorithm in order to extend the conventional MLC approach to mixture density model. EM assumes that the overall distribution could be decomposed into infinite parametric distributions. The model should be firstly assumed that whole distribution could be separated into infinite parametric density distributions, then by EM iterative computation the maximum likelihood parameters of each proportional distribution can be estimated. Better parameter estimates can be obtained by exploiting a large number of unlabeled samples in addition to training samples using the EM algorithm under the mixture model. Furthermore, we present out the framework and detailed process of EM MLC for remote sensing image classification. By experimental case, the EM MLC classification algorithm is compared with conventional MLC algorithm qualitatively and quantitatively. The results show that the EM MLC could obtain higher accuracy than MLC.