  Supervised learning for semantic segmentation requires a large number of
labeled samples, which is difficult to obtain in the field of remote sensing.
Self-supervised learning (SSL), can be used to solve such problems by
pre-training a general model with a large number of unlabeled images and then
fine-tuning it on a downstream task with very few labeled samples. Contrastive
learning is a typical method of SSL that can learn general invariant features.
However, most existing contrastive learning methods are designed for
classification tasks to obtain an image-level representation, which may be
suboptimal for semantic segmentation tasks requiring pixel-level
discrimination. Therefore, we propose a global style and local matching
contrastive learning network (GLCNet) for remote sensing image semantic
segmentation. Specifically, 1) the global style contrastive learning module is
used to better learn an image-level representation, as we consider that style
features can better represent the overall image features. 2) The local features
matching contrastive learning module is designed to learn representations of
local regions, which is beneficial for semantic segmentation. The experimental
results show that our method mostly outperforms SOTA self-supervised methods
and the ImageNet pre-training method. Specifically, with 1\% annotation from
the original dataset, our approach improves Kappa by 6\% on the ISPRS Potsdam
dataset relative to the existing baseline. Moreover, our method outperforms
supervised learning methods when there are some differences between the
datasets of upstream tasks and downstream tasks. Since SSL could directly learn
the essential characteristics of data from unlabeled data, which is easy to
obtain in the remote sensing field, this may be of great significance for tasks
such as global mapping. The source code is available at
https://github.com/GeoX-Lab/G-RSIM.
