该文提出了一种适用于多光谱遥感图象分类的自适应最小距离算法.这一算法通过对样本集合的自适应精细划分来调整最小距离分类器的参数.对TM遥感图象分类的实验结果表明,该方法对16类数据进行有监督分类,精度可达92.9%,适用于多类别遥感图象分类.

This article presents an adaptive min-distance algorithm to classify multi-spectral remote-sensing images. This method approximates the distribution of the classes by dividing the sample sets, and adjusts the parameters of the min-distance classifier adaptively. Experiments with TM remote-sensing images demonstrate that this approach achieves an accuracy of 92.9% in the supervised classification of 16 classes. The experimental results verify the applicability of this approach in classifying of multicategory remote-sensing images.