In recent years, convolutional neural networks (CNNs) have achieved great success in hyperspectral image (HSI) classification attributed to their unparalleled capacity to extract the local information. However, to successfully learn the high-level semantic image features, they always require massive amounts of manually labeled data during the training process, which is expensive, scarce, and impractical, and severely hinders the improvement of supervised deep learning methods. To alleviate these burdens, we present self-supervised learning (SSL) methods for HSI classification by a pretraining model using extensive unlabeled data and fine-tuning the HSI target classification. In this article, we propose a new method for learning image characteristics by training a CNN to recognize the image scale (IS) that is applied to the HSIs. In addition, we propose a multipretext task (MT) method to learn stable and good feature representations combing two different pretext task methods and contrastive loss function. We evaluate the proposed methods in SSL benchmarks on four benchmark HSIs datasets. The experiment results demonstrate that the proposed methods outperform the traditional supervised deep learning methods when large amounts of unlabeled HSIs data are used. Moreover, it demonstrates that the SSL method is promising to alleviate dependence on manually labeled data of HSI classification. Finally, our research contributes to the creation and refinement of SSL methods for pretextual tasks within the HSIs community.