A Study of FasART Neuro-fuzzy Networks for Supervised Classification of Remotely Sensed Images
FasART模糊神经网络用于遥感图象监督分类的研究

The paper explains briefly that the remotely sensed data is non linear, and the practice of its classification by mans eyes is a process of the fuzzy inference. The fuzzy neural networks has a theory dominance, because it accords with the nature rule of classification of remotely sensed images. Analyses the architecture and principles of fuzzy ART, fuzzy ARTMAP. Discusses in detail that FasART is a neural networks based on fuzzy logic system. Put forward a simplified FasART architecture and change the general method of remotely sensed data fuzzification. With the testing of the CBERS -1 data, the results declares that the simple FasART model can be used to supervised classification of the remotely sensed images. The precision of the classification is higher than that of fuzzy ARTMAP and K means. The classification of FasART model has better stabilization and anti jamming, and has capability of dealing with non linear data especially.