Abstract
An image classification method with Chaos Neural Network (CNN) is proposed. CNN allows to find more close to a globally optimum solution than Back Propagation Neural Network (BPNN) due to the fact that behavier of the solution from CNN is chaotic so that the solution may get out from local minima. In order to determine the parameters required for CNN, training samples are used. The required parameters for the simulation are calculated beforehand using the training samples. Experimental results with simulation and actual satellite imagery data of Landsat TM show that the proposed CNN based image classification performance are better than BPNN performance by 18.2-54.3% for the simulation data while by 13.4% for Landsat-5 TM data, respectively.
