  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, time-consuming and expensive.
However, existing semi-supervised learning methods pay limited attention to the
quality of pseudo-labels during training even though the quality of training
data is 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 for land cover classification.
Meanwhile, the proposed semi-supervised learning approach uses multiple network
architectures to increase the diversity of pseudo labels. The proposed
semi-supervised learning approach significantly improves the performance of
land cover classification compared to the classic semi-supervised learning
methods and even outperforms fully supervised learning with a complete set of
labelled imagery of the benchmark Potsdam land cover dataset.
