Hyperspectral image classification via Elastic Net Regression and bilateral filtering

In this paper, we present a two phase hyper-spectral image classification method combining Elastic Net Regression and spectral spatial bilateral filtering. Initially the proposed method computes the pixel-based classification to evaluate the quality of the selected bands identified by Elastic Net regularized multinomial logistic regression. To incorporate the spatial information, the optimization probabilities problem are obtained by performing bilateral filtering on the initial probability maps, with the principal components of the hyper-spectral image serving as the color guidance image. The proposed classification approach has shown the remarkable classification accuracy with less resources and low computation time.