Semi-supervised learning is designed to help reduce the cost of the manual
labelling process by exploiting the use of useful features from a large
quantity of unlabelled data during training. Since pixel-level manual labelling
in large-scale remote sensing imagery is expensive, semi-supervised learning
becomes an appropriate solution to this. However, most of the existing
semi-supervised learning methods still lack efficient perturbation methods to
promote diversity of features and the precision of pseudo labels during
training. In order to fill this gap, we propose DiverseNet architectures which
explore multi-head and multi-model semi-supervised learning algorithms by
simultaneously promoting precision and diversity during training. The two
proposed methods of DiverseNet, namely the DiverseHead and DiverseModel,
achieve the highest semantic segmentation performance in four widely utilised
remote sensing imagery data sets compared to state-of-the-art semi-supervised
learning methods. Meanwhile, the proposed DiverseHead architecture is
relatively lightweight in terms of parameter space compared to the
state-of-the-art methods whilst reaching high-performance results for all the
tested data sets.

通过同时提高训练过程中的精确度和多样性，我们提出的 DiverseNet 架构可以使用多头和多模型的半监督学习算法，实现与半监督学习方法相比更高的语义分割性能，并且相对于最先进的方法，我们提出的 DiverseHead 架构在参数空间上相对较轻，同时能够在所有测试数据集上获得高性能的结果。