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中文摘要：
在对数据进行归一化处理的基础上,将概率神经网络用于遥感影像分类,并探讨样本区的选择和高斯基函数标准差对分类精度的影响。用西藏波密地区1999年的TM遥感影像进行分类试验,并将分类结果和经典的最大似然法进行比较。结果表明：概率神经网络的总体分类精度和Kappa系数分别为94.5%和0.934,取得了较为理想的识别和分类效果。
英文摘要：
This paper has analyzed the remote sensing classification using the probabilistic neural network（PNN） on the basis of data normalization,for the best classification accuracy,the picking of sample area and the standard deviation of the basis Gauss function has been discussed.PNN classification model was applied to classify the TM image in Tibet.Based on error matrix,the classification result of the maximum likelihood was contrasted with that of PNN model.The results show that the overall accuracy and Kappa coefficient of PNN model reach 94.5% and 0.934,respectively,is superior to that of the traditional Maximum likelihood method.
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