Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification

This paper investigates the effectiveness of the genetic algorithm evolved neural network classifier and its application to the land cover classification of remotely sensed multispectral imagery. First, the key issues of the algorithm and the general procedures are described in detail. Our methodology adopts a real coded GA strategy and hybrid with a back propagation (BP) algorithm. The genetic operators are carefully designed to optimize the neural network, avoiding premature convergence and permutation problems. Second, a SPOT-4 XS imagery is employed to evaluate its accuracy. Traditional classification algorithms, such as maximum likelihood classifier, back propagation neural network classifier, are also involved for a comparison purpose. Based on an evaluation of the user’s accuracy and kappa statistic of different classifiers, the superiority of applying the discussed genetic algorithm-based classifier for simple land cover classification using multi-spectral imagery data is established. Thirdly, a more complicate experiment on CBERS (China-Brazil Earth Resources Satellite) data and discussion also demonstrates that carefully designed genetic algorithm-based neural network outperforms than gradient descent-based neural network. This has been supported by the analysis of the changes of connection weights and biases of the neural network. Finally, some concluding remarks and suggestions are also presented.