Using genetic differential competitive learning for unsupervised training in multispectral image classification systems

This paper describes a genetic differential competitive learning algorithm, which is proposed to prevent fixation to the local minima and improve the unsupervised training results for the classification of remotely sensed data. The differential competitive learning (DCL) combines competitive and differential-Hebbian learning and represents a neural version of adaptive delta modulation. This learning law uses the neural signal velocity as a local unsupervised reinforcement mechanism. The Jeffries-Matusita (J-M) distance, which is a measure of statistical separability of pairs of the `trained´ clusters, is used for the evaluation of the proposed algorithm. The Landsat Thematic Mapper (TM) data will be used for simulation to show the effectiveness of the algorithm