Abstract
In this paper, a weighted random subspace method (RSM) with automatic subspace dimensionality selection has been proposed for classifying hyperspectral image data. The dimensionality selection method is based on the importance distribution of dimensionality estimated by kernel smoothing technique during the algorithm training. Two feature weighting methods based on normalized re-substitution accuracy and Fisher's LDA separability are introduced for improving the original RSM. Experimental result shows that the proposed algorithm outperforms the original random subspace method.
