最大似然(ML)分类方法是一种典型的基于统计分析的监督分类方法, 从理论上讲,具有最小出错率与最高分类精度的特点。但最大似然分类方法是以数据的正态分布假设为前提的, 这在真实遥感数据中很难满足, 从而导致分类精度下降。根据数据分布可以以任意精度由多个正态分布的线性组合表示, 对最大似然分布的数据分布进行修正, 既提高了数据模型的正确性, 又充分利用正态分布的优点。最大似然分类方法的训练样本挑选也具有一定的随意性和主观性, 先验概率直接影响分类结果, 而且对整幅图像采用同样的先验概率会导致分类精度下降。针对训练样本的选择问题, 先用ISODATA聚类算法对数据进行聚类, 对比参考分类图像选择训练区域, 一方面利用聚类结果可以选择性质均匀的区域, 另一方面使得样本的选择变得简单, 最后进行了遥感数据的分类实验。实验结果证明了该方法不仅可以实现遥感数据的分类, 而且具有较高的总体分类精度和Kappa系数。

Maximum Likelihood (ML) classification method is based on the assumption that the data are normally distributed, which is not always true for the realistic remote sensing data, and may result in decrease of classification accuracy.The classification results are impacted directly by the prior probability.The selection of training samples is somewhat stochastic and subjective.The ML method uses the same prior probability for the whole image, which will also reduce the classification accuracy.Theoretically, every smooth density function can be approximated to within any accuracy by such a mixture of normal densities.Thus the first problem of ML can be solved by using a combination of several normal functions instead of one.In this way, a very general capability can be provided, while still maintaining the convenient properties of the normal assumption.For the second problem, ISODATA is used to make a clustering image of the original data, after that, one can select the training areas of the image by comparing with the reference image.At last, the result of experiment shows that the proposed methods can not only realize the classification of remote sensing image but also achieve very high accuracy visually and mathematically in overall accuracy and Kappa coefficient.