  Semi-supervised learning aims to help reduce the cost of the manual labelling
process by leveraging valuable features extracted from a substantial pool of
unlabeled data alongside a limited set of labelled data during the training
phase. 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 consistency learning frameworks based on network
perturbation are very bulky. There is still a lack of lightweight and efficient
perturbation methods to promote the diversity of features and the precision of
pseudo labels during training. In order to fill this gap, we propose DiverseNet
which explores multi-head and multi-model semi-supervised learning algorithms
by simultaneously enhancing precision and diversity during training. The two
proposed methods in the DiverseNet family, namely DiverseHead and DiverseModel,
both achieve the better 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 simple and 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.
