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.
The goal of image segmentation is to process the data given by the pixels so as to obtain a 'meaningful' partitioning of the image. The pixels of an image are represented by points in an n-dimensional space (spectral space). There is no universally accepted definition for texture, here texture means relation among pixels, given by the statistic of the image. In this work, an algorithm has been developed for segmenting color images (or multispectral), after performing a clustering over the set of points in spectral space. The algorithm captures texture information in order to segment the image. Finally, results obtained with synthetic and real aerial images are shown.
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