Deep learning has been extensively applied in hyperspectral image (HSI) classification for its excellent representation ability. However, the existing training scheme generally adds a supervised classifier to the last layer of the network, so it is difficult to acquire full-scale fine-grained details and coarse-grained semantic information. Moreover, the robust performance of deep learning is commonly supported by numerous samples, so the effective discriminant features cannot be well learned with small class-imbalanced samples. To solve the above problems, a deeply-supervised pseudo learning framework (DSPL) is proposed, in which a deep supervision global learning network with pair-weighted loss is designed to achieve a stronger prediction on small class-imbalanced datasets, while this architecture of deep supervision can facilitate model generalization. To increase the diversity of samples, a semi-supervised learning method with confidence pseudo labels is proposed, capable of screening for more valid unlabeled samples and synthesizing some new mixed samples. To be more specific, the cost loss function consists of the supervised team (i.e., the labeled loss) and semi-supervised consistency regularization team (i.e., the unlabeled loss and the mixed loss), which can significantly enhance the generalization of the network by all useful samples. As revealed by the experimental results, the DSPL is better than other advanced methods on the Indian Pines (highest OA of 99.54% with 5% samples), the Pavia University (highest OA of 99.79% with 0.5% samples), as well as the Houston University 2013 (highest OA of 99.32% with 5% samples).

深度学习因其出色的表示能力而被广泛应用于高光谱图像（HSI）分类。然而，现有的训练方案一般在网络的最后一层添加有监督分类器，因此难以获得全尺度的细粒度细节和粗粒度语义信息。此外，深度学习的鲁棒性能通常得到大量样本的支持，因此使用小的类不平衡样本无法很好地学习有效的判别特征。为了解决上述问题，提出了深度监督伪学习框架（DSPL），其中设计了具有对加权损失的深度监督全局学习网络，以实现对小类不平衡数据集的更强预测，而这种深度监督架构可以促进模型泛化。为了增加样本的多样性，提出了一种带有置信度伪标签的半监督学习方法，能够筛选出更多有效的未标记样本并合成一些新的混合样本。更具体地说，成本损失函数由受监督的团队组成（即标记损失）和半监督一致性正则化团队（即未标记损失和混合损失），这可以显着增强所有有用样本对网络的泛化能力。实验结果表明，DSPL 在 Indian Pines（最高 OA 为 99.54%，样本为 5%）、帕维亚大学（最高 OA 为 99.79%，样本为 0.5%）以及休斯顿大学 2013 年（最高 OA 为 99.32%，样本为 5%）。