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.
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