遥感图像分类方法通常采用监督的学习算法，它需要人工选取训练样本，比较繁琐，而且有时很难得到；而非监督学习算法的分类精度通常很难令人满意。
The supervised learning algorithm was usually used for remote sensing image classification, but its training samples need to be chosen by manual, which was boring and sometimes even difficult.
对于输入模式的划分，在前人的基础上，采用一种新型且有效的方法选取训练样本，对于节假日的负荷，本文对其进行另外的讨论，并提出了一种基于插值的模式选取办法。
In the paper, a new and sufficient method about the selection of the training sample is proposed and also the division of inputting in festivals is operated with a new method by using interpolation.
本文分析了模块2dpca在计算训练样本总体散布矩阵和本征向量选取方面的缺陷，提出了一种改进的模块2dpca算法。
In this paper, the defects of modular 2dpca about computing the total scatter matrix of training samples and selecting eigenvectors are analyzed. An improved modular 2dpca algorithm is presented.
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