A combination of probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithms to map hydrothermal alteration zones using ASTER data

Abstract

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.