Feature selection of hyperspectral data by considering the integration of genetic algorithms and particle swarm optimization

At this stage of data acquisition, we are in the era of massive automatic data collection, systematically obtaining many measurements, not knowing which data are appropriate for a problem at hand. In this paper, a feature selection approach is discussed. The approach is based on the integration of a Genetic Algorithm and Particle Swarm Optimization. Support Vector Machine classifier is used as fitness function and its corresponding overall accuracy on validation samples is used as fitness value, in order to evaluate the efficiency of different groups of bands. The approach is carried out on the wellknown Salinas hyperspectral data set. Results confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy within an acceptable CPU processing time without requiring the number of desired features to be set a priori by users.