Fast forward feature selection for the nonlinear classification of hyperspectral images
This work addresses the challenge of efficient feature selection for hyperspectral image classification, which is incremental as it builds on existing GMM methods with optimizations for speed.
The paper tackles the problem of feature selection for classifying hyperspectral images by proposing a fast forward algorithm based on a Gaussian mixture model classifier, which achieves high classification accuracy and processing speed while using few spectral channels, as demonstrated on two real datasets.
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.