Semi- and Self-Supervised Metric Learning for Remote Sensing Applications

Abstract

Earth data collection from satellites and aircraft has exponentially grown, but a substantial portion of it remains unlabeled. This has prompted the remote sensing community to explore effective methods for leveraging unlabeled data. In our prior investigation, we evaluated various deep semi-supervised learning algorithms on two very high-resolution (VHR) optical datasets (UCM and AID). Notably, the CoMatch algorithm demonstrated the highest accuracy, motivating further exploration. This letter extends our earlier work by integrating the established class-aware contrastive semi-supervised learning framework (CoMatch + CCSSL) into CoMatch and introducing a new triplet metric learning loss (CoMatch + Triplet). CoMatch + Triplet excelled with 93.2% accuracy on UCM, while CoMatch led with 92.19% on AID. The addition of the triplet loss can produce a clearer separation of the samples from different classes in the embedding space at very early learning stages, being able to learn faster and getting maximum performance with few iterations. The exploration of diverse semi- and self-supervised training methodologies presented in this work sheds light on the strengths and limitations of these approaches, enhancing our understanding of their applicability in remote sensing applications.