Classification of remote sensing imagery using genetic algorithms and neural networks

This paper presents the application of Neural Networks (ANN) and introduces Genetic Algorithms (GA) to agricultural land use classification. Daedalus ATM data at 1 m resolution, has been used to train and test the algorithms. Layered feed forward ANN's have been found to have good generalization properties. The Backpropagation (BP) algorithm is very susceptible to initial conditions and the problem of local minima. Therefore this technique alone is not the best method for the classification of complex multi-dimensional data sets. This paper applies an evolutionary technique for training feed forward ANN's, which searches the error space for a more likely initialization point. Optimization and learning problems are two techniques where ANN's and GA's have excelled. Evolutionary Artificial Neural Networks, introduced in this paper, can be thought of as being a cross between ANNs and GAs. The weights and biases are updated by applying the mutation genetic operator and can be compared with the principle of natural biological life, where survival of the fittest leads to a near optimum ANN. These weights and biases are then adopted by the BP algorithm to quickly converge on the global minima.