A New Multiple Classifier System for Semi-supervised Analysis of Hyperspectral Images

In this work, we propose a new semi-supervised algorithm for remotely sensed hyperspectral image classification which belongs to the family of multiple classifier systems. The proposed approach combines the output of two well-established discriminative classifiers: sparse multinomial logistic regression (SMLR) and quadratic discriminant analysis (QDA). Our approach follows a two-step strategy. First, both SMLR and QDA are trained from the same set of labeled training samples and make predictions for the unlabeled samples in the image. Second, the set of unlabeled training samples is expanded by combining the estimates obtained by both classifiers in the previous step. The effectiveness of the proposed method is evaluated via experiments with a widely used hyperspectral image, collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in Indiana. Our results indicate that the proposed multiple classifier method provides state-of-the-art performance when compared to other methods.