提出了一种基于局部自动搜索和光谱匹配技术训练样本纯化的BP网络分类方法。利用影像的空间信息在图像局部范围内自动搜索和选择最佳样区位置，再用光谱匹配对寻找到的最佳样区在光谱空间上进一步纯化。从空间和光谱两个角度对样区进行了纯化，使得训练样本更适合遥感图像分类的要求，最后利用BP网络对遥感图像进行分类。实验结果证明，原始遥感图像经过样区纯化算法处理后，目视判读效果和数值分析都表明提高了分类精度。

This paper proposes a supervised classification method for remote sensing images based on locally automatically searching training samples and spectral matching technique. The best training samples are searched and selected on the whole image by the local space information and spectral matching, and then they are purified on spectral domains. Both spatial and spectral information are purified to enable the training sample to meet the requirement for classification at the best of times. An experiment for TM image classification based on BP neural networks has been conducted to validate the procedure. It can be seen from our experiment that the classifying results are improved from the observation of naked eye and the numerical analysis. So the proposed approach has practical application value to some extend for it's simple and high efficiency.