Genetic algorithm for accomplishing feature extraction of hyperspectral data using texture information

An algorithm to project a high dimensional space (hyperspectral space) to one with few dimensions is studied, therefore most of the information for an unsupervised classification is kept in the process. The algorithm consists of two parts: first, since the experience shows that bands that are close in the spectrum have redundant information, groups of adjacent bands are taken and a genetic algorithm is applied in order to obtain the best representative feature for each group, in the sense of maximizing the separability among clusters. The second part consists in applying the genetic algorithm again, but this time context information is included in the process. The results are compared with the usual methods of feature selection and extraction.