  Dimensionality reduction is an important preprocessing step of the
hyperspectral images classification (HSI), it is inevitable task. Some methods
use feature selection or extraction algorithms based on spectral and spatial
information. In this paper, we introduce a new methodology for dimensionality
reduction and classification of HSI taking into account both spectral and
spatial information based on mutual information. We characterise the spatial
information by the texture features extracted from the grey level cooccurrence
matrix (GLCM); we use Homogeneity, Contrast, Correlation and Energy. For
classification, we use support vector machine (SVM). The experiments are
performed on three well-known hyperspectral benchmark datasets. The proposed
algorithm is compared with the state of the art methods. The obtained results
of this fusion show that our method outperforms the other approaches by
increasing the classification accuracy in a good timing. This method may be
improved for more performance
  Keywords: hyperspectral images; classification; spectral and spatial
features; grey level cooccurrence matrix; GLCM; mutual information; support
vector machine; SVM.
