A large number of training samples can effectively alleviate the overfitting of the model and improve the classification effect. A lot of high-precision training samples are rapidly amplified by using homogenous regions of different scales. The support vector machine classifier is trained with the initial labeled samples and amplified samples to achieve the effective classification of hyperspectral data. The majority of high-precision training samples based on Pavia University data, Salinas data and Indian Pines data can be obtained by this method, and the accuracy is above 99%, 99% and 97% respectively. The experiment results show that the large number of pseudo-label samples amplified by the proposed method can effectively train the SVM classifier and improve the classification effect.