For the selection of training samples in vegetation classification based on hyperspectral remote sensing, this paper investigates and analyzes the methods for selection of training samples in common use, presents two new selection (purification) methods for training samples, and then testifies the validity of these methods which are combined with specific OMIS-I hyperspectral remote sensing data.