针对单一分类器的不足,文中采用最短距离分类器、马氏距分类器、K-均值分类器对多光谱遥感影像进行分类,并在测量级的融合方法下进行多种分类器融合分类实验,最后采用混淆矩阵进行分类结果精度评价。实验结果表明:多分类器融合的遥感影像分类方法在精度上高于单一分类器分类。 Aiming at the shortcomings of single classifier, the multi-spectral remote sensing images are classified by using the shortest distance classifier, the Mahalanobis distance classifier and the K-means classifier, and multiple classifier fusion classification experiments are conducted under the measurement level fusion method. Finally, the confusion matrix is ​​used to evaluate the accuracy of classification results. The experimental results show that the classification accuracy of multi-classifier fusion is higher than that of single classifier classification.