Textural feature selection by joint mutual information based on Gaussian mixture model for multispectral image classification

Textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. However, the selection of informative ones with highly discriminatory ability to improve the classification accuracy is still one of the well-known problems in remote sensing.In this paper, we propose a new method based on the Gaussian mixture model (GMM) in calculating Shannon’s mutual information between multiple features and the output class labels. We apply this, in a real context, to a textural feature selection algorithm for multispectral image classification so as to produce digital thematic maps for cartography exploitation. The input candidate features are extracted from an HRV-XS SPOT image of a forest area in Rabat, Morocco, using wavelet packet transform (WPT) and the gray level cooccurrence matrix (GLCM). The retained classifier is the support vectors machine (SVM). The results show that the selected textural features, using our proposed method, make the largest contribution to improve the classification accuracy than the selected ones by mutual information between individual variables. The use of spectral information only leads to poor performances.