  This study is focused on applying genetic algorithms (GA) to model and band
selection in hyperspectral image classification. We use a forensic-inspired
data set of seven hyperspectral images with blood and five visually similar
substances to test GA-optimised classifiers in two scenarios: when the training
and test data come from the same image and when they come from different
images, which is a more challenging task due to significant spectra
differences. In our experiments we compare GA with a classic model optimisation
through grid search. Our results show that GA-based model optimisation can
reduce the number of bands and create an accurate classifier that outperforms
the GS-based reference models, provided that during model optimisation it has
access to examples similar to test data. We illustrate this with experiment
highlighting the importance of a validation set.
