Recently, pseudolabel-based deep learning methods have shown excellent performance in semi-supervised hyperspectral image (HSI) classification. These methods usually select high-confidence unlabeled samples to help optimize backbone classification networks. However, a large number of remaining low-confidence unlabeled samples, which contain rich land-covers information, are underused. In this article, we propose a pseudolabel-based unreliable sample learning (PUSL) method to fully exploit low-confidence unlabeled samples for semi-supervised HSI classification. First, to avoid overfitting the s...
