【摘要】 对比学习作为一种有效的预训练方式，通过区分正负样本对，促使编码器学到良好的视觉表征，使用少量样本即可获得优越的模型性能。然而现有的对比学习方法，仅通过数据增强这一单一方式构造正样本，导致所构建的正样本缺乏真实性和丰富性，限制了模型的性能。为缓解这一问题，提出一种基于正样本重构的对比学习框架。通过构造样本支持集，搜索当前样本的最近邻域作为正样本，将正样本扩充到真实场景，进一步提升算法的分类性能。实验结果表明，在公开的高光谱图像数据集Pavia University和Houston上，所提出的算法仅使用每类10个标记样本即可获得优越的分类效果，在减少算法数据依赖的同时，提升了模型性能。更多还原

【Abstract】 Contrast learning, as an effective pre-training method, promotes the encoder to learn good visual representation by distinguishing positive and negative sample pairs, and superior model performance can be obtained by using a small number of samples. However, the existing contrast learning method only constructs positive samples through data enhancement, which leads to the lack of authenticity and richness of the constructed positive samples, and limits the performance of the model. To alleviate this problem, a contrastive learning framework based on positive sample reconstruction was proposed. By constructing the sample support set, the nearest neighbor of the current sample was searched as the positive sample, and the positive sample was extended to the real scene to further improve the classification performance of the algorithm. The experimental results show that in the published hyperspectral image datasets Pavia University and Houston, the proposed algorithm can obtain superior classification effect by using only 10 labeled samples for each category, which reduces the data dependence of the algorithm and improves the model performance.更多还原