Genetic classifiers for remotely sensed images: comparison with standard methods

In this article the eVectiveness of some recently developed genetic algorithm-based pattern classiŽ ers was investigated in the domain of satellite imagery which usually have complex and overlapping class boundaries. Landsat data, SPOT image and IRS image are considered as input. The superiority of these classiŽ ers over k-NN rule, Bayes’ maximum likelihood classiŽ er and multilayer perceptron (MLP) for partitioning diVerent landcover types is established. Results based on producer’s accuracy (percentage recognition score), user’s accuracy and kappa values are provided. Incorporation of the concept of variable length chromosomes and chromosome discrimination led to superior performance in terms of automatic evolution of the number of hyperplanes for modelling the class boundaries, and the convergence time. This non-parametric classiŽ er requires very little a priori information, unlike k-NN rule and MLP (where the performance depends heavily on the value of k and the architecture, respectively), and Bayes’ maximum likelihood classiŽ er (where assumptions regarding the class distribution functions need to be made).