A semi-supervised network for object segmentation of remote sensing images

Recently, object segmentation of remote sensing images has achieved great progress in many fields, such as transportation, natural resource, ecology, et al. A lot of works mainly performed object segmentation in fully-supervised mode. However, training models in such mode usually need craft large-scale annotations, which is usually an expensive work and costs much time. In this paper, a novel semi-supervised network for object segmentation of remote sensing images is proposed, which is only fed with a small amount of labeled data and relatively more unlabeled data. Rather than using the same architecture as previous semi-supervised works, we exploit two networks with different architectures, i.e. CNN and Transformer, as the cross-supervised models. Moreover, three types of loss functions, namely fully-supervised loss, cross-supervised loss and consistency loss, are introduced to enhance the model's robustness. The effectiveness of our proposed method is evaluated on two annotated remote sensing datasets, outperforming several state-of-the-art semi-supervised approaches.