An unsupervised neural network classifier and its application in remote sensing

Neural networks have been used to classify high resolution remote-sensed data. Experiments have demonstrated the potential of neural networks for clustering a large number of ground cover instances using supervised methods. The paper describes a new algorithm of unsupervised learning, based on artificial neural networks. Its performance has been compared with the competitive learning algorithm. The efficiency of this approach has been demonstrated through experimental results obtained on the real-world of multispectral remote sensing data.<
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