Research on Hyperspectral Data Classification Based on Quantum Counter Propagation Neural Network

Abstract

It proposes the model and learning algorithm of Quantum Counter Propagation Neural Network and applies which in hyperspectral data classification as well. On one hand, introducing quantum theory into the structure or training process of Counter Propagation Neural Network with regard to improving structure and capacity of Classical Neural Network, enhancing learning and generalization ability of it. On the other hand, establishing a new topological structure and training algorithm of Quantum Counter Propagation Neural Network by the means of quoting the thought, concept and principles of quantum theory directly. To complete the experiment of hyperspectral data classification with three ways and the result shows that effects of Quantum Counter Propagation Neural Network is superior to the traditional classification.