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.
讨论了径向基函数中心的选取，构造了改进的RBF网络对训练样本和测试样本进行识别。
The choice of the center of radial basis function, constructing an improved RBF network and its application to recognize the trained samples and test samples were discussed.
阐述了利用神经网络建立结构损伤辨识系统的过程 ，并对系统建立过程中训练样本的选取和预处理问题进行了说明。
Using this method, a damage identification system of reinforced concrete board structure is set up, and the results of identification show that the system is available.
阐述了利用神经网络建立结构损伤辨识系统的过程 ，并对系统建立过程中训练样本的选取和预处理问题进行了说明。
Using this method, a damage identification system of reinforced concrete board structure is set up, and the results of identification show that the system is available.
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