The title of the thesis focuses the attention on hyperspectral image segmentation, that is, we want to detect salient regions in a hyperspectral image and isolate them as accurate as possible. This purpose presents two main problems: Firstly, the fact of using hyperspectral imaging not only give us a huge amount of information, but we also have to face the problem of selecting somehow the information avoiding redundancies.Secondly, the problem of segmentation strictly speaking is still a challenging question whatever the input image would be.This thesis is focused on solving the whole process by means of building an image processing method that analyses and optimises the information acquired by a multispectral device. After that, it detects the main regions that are present in the scene in an image segmentation procedure. Therefore, this work will be divided into two parts. In the first part, an approach for selecting the most relevant subset of input bands will be presented. In the second part, this reduced representation of the initial bands will be the input data of a segmentation method.Finally, the main contributions of this PhD work could be briefly summarised as follows. On the one hand, we have proposed a pre-processing stage with an unsupervised band selection approach based on information measures that reduces considerably the amount of data. This approach has been successfully compared with well-known algorithms of the literature, showing its good performance with regard to pixel image classification tasks. On the other hand, after the band selection stage, two unsupervised segmentation procedures for detecting the main parts in multispectral images have been also developed. Regarding to this segmentation part, we have mainly contributed with two measures of similarity among regions. An objective functional for selecting an optimal (or close to optimal) partition of the image is another relevant contribution too.