Hyperspectral remote sensing technologies have many applications in land cover classification and study their changes. With recent developments and create images with high spatial resolution, it is necessary the use of both spatial and spectral information in hyperspectral image classification. In this paper, we have evaluated the effect of dimensionality reduction using genetic algorithm on spectral-spatial classification of hyperspectral imagery. So far, among the various algorithms spectral-spatial classification of hyperspectral images, three segmentation algorithms, watershed, hierarchical and Minimum Spanning Forest (MSF) based on markers, combined with Support Vector Machines (SVM) to achieve the best results. In the proposed approach, the dimension of hyperspectral images is first reduced by using genetic algorithm. Then, the three mentioned segmentation algorithms are applied on the resulting bands. Finally, the obtained segmentation maps are combined with SVM classification map using majority voting rule. The proposed approach was implemented on three hyperspectral data sets, the Pavia dataset, the Telops dataset, and the DC Mall dataset. The obtained experimental results indicate the superiority use of reduced bands in MSF based on markers algorithm and all bands in watershed and hierarchical based on markers algorithms.