  A fast forward feature selection algorithm is presented in this paper. It is
based on a Gaussian mixture model (GMM) classifier. GMM are used for
classifying hyperspectral images. The algorithm selects iteratively spectral
features that maximizes an estimation of the classification rate. The
estimation is done using the k-fold cross validation. In order to perform fast
in terms of computing time, an efficient implementation is proposed. First, the
GMM can be updated when the estimation of the classification rate is computed,
rather than re-estimate the full model. Secondly, using marginalization of the
GMM, sub models can be directly obtained from the full model learned with all
the spectral features. Experimental results for two real hyperspectral data
sets show that the method performs very well in terms of classification
accuracy and processing time. Furthermore, the extracted model contains very
few spectral channels.
