PNN is a feed-forward neural network in which there is an important parameter called smoothing parameter. This work implemented a combination of PNN with PSO optimization in order to estimate unique smoothing parameters for each SWIR bands of ASTER image and classified the ASTER image to different hydrothermal alteration zones (argillic, phyllic, propylitic and vegetation covering). The stydy area is a part of Kerman Cenozoic Magmatic Arc (KCMA) which contains several known porphyry copper deposits. Confusion matrix was used to validate the results of PNN-PSO algorithm and it presented the overall accuracy of 76.9% for developed algorithm. Also, comparing the obtained results with traditional methods of remote sensing (SPCA) showed that PNN-PSO is superior to SPCA technique. In fact, SPCA could not dicriminate different hydrothermal alterations while the present work proved that PNN-PSO is a good tool for classfication of argillic, phyllic, propylitic and vegetation covering.

中文翻译：

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概率神经网络（PNN）和粒子群优化（PSO）算法相结合，可使用ASTER数据绘制热液蚀变带图

PNN是前馈神经网络，其中有一个重要的参数称为平滑参数。这项工作实现了PNN与PSO优化的组合，以便为ASTER图像的每个SWIR波段估计唯一的平滑参数，并将ASTER图像分类到不同的热液蚀变带（泥质，叶绿素，炔丙基和植被覆盖）。麦田为Kerman新生代岩浆弧（KCMA）的一部分，该弧包含几个已知的斑岩铜矿床。混淆矩阵用于验证PNN-PSO算法的结果，对于已开发的算法，其总体准确性为76.9％。此外，将获得的结果与传统的遥感方法（SPCA）进行比较表明，PNN-PSO优于SPCA技术。事实上，