Stochastic learning algorithms for the classification of remote-sensing images

The weight perturbation learning algorithm was formerly developed by hardware designers for its friendly features in the perspective of the analog on-chip implementation. Therefore it has not been validated in real-world applications but only on test problems. To significantly increase its attitude for the on-chip implementation, we proposed a local learning rate adaptation technique, which anyway, increases also the performance. At the same time to demonstrate the efficiency of the weight perturbation algorithm, in Ibis paper we report the results of the application of the proposed algorithm to the classification of remote-sensing images. Our results compare favorably with those reported in the literature and demonstrate the soundness of the proposed approach.