Semi-supervised learning has been well developed to help reduce the cost of manual labelling by exploiting a large quantity of unlabelled data. Especially in the application of land cover classification, pixel-level manual labelling in large-scale imagery is labour-intensive and expensive. However, the existing semi-supervised learning methods pay limited attention to the quality of pseudo-labels whilst supervising the network. That is, nevertheless, one of the critical factors determining network performance. In order to fill this gap, we develop a confidence-guided semi-supervised learning (CGSSL) approach to make use of high-confidence pseudo labels and reduce the negative effect of low-confidence ones on training the land cover classification network. Meanwhile, the proposed semi-supervised learning approach uses multiple network architectures to increase pseudo-label diversity. The proposed semi-supervised learning approach significantly improves the performance of land cover classification compared to the classical semi-supervised learning methods in computer vision and even outperforms fully supervised learning with a complete set of labelled imagery of the benchmark Potsdam land cover data set.

翻译：半监督学习已被广泛发展，通过利用大量未标注数据来降低人工标注成本。特别是在土地覆盖分类应用中，大规模影像的像素级人工标注耗时且昂贵。然而，现有的半监督学习方法在监督网络时对伪标签质量的关注有限，而这恰恰是决定网络性能的关键因素之一。为填补这一空白，我们提出了一种置信引导的半监督学习（CGSSL）方法，利用高置信度伪标签并降低低置信度伪标签对土地覆盖分类网络训练的负面影响。同时，所提半监督学习方法采用多种网络架构来增加伪标签的多样性。与计算机视觉领域的经典半监督学习方法相比，该方法显著提升了土地覆盖分类的性能，甚至在基准Potsdam土地覆盖数据集上，使用完整标注影像时，其性能超越了全监督学习。