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.

中文翻译：

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遥感应用的半监督和自监督度量学习

从卫星和飞机收集的地球数据呈指数级增长，但其中很大一部分仍未标记。这促使遥感界探索利用未标记数据的有效方法。在我们之前的调查中，我们在两个超高分辨率 (VHR) 光学数据集（UCM 和 AID）上评估了各种深度半监督学习算法。值得注意的是，CoMatch 算法表现出最高的准确性，激发了进一步的探索。这封信扩展了我们早期的工作，将已建立的类感知对比半监督学习框架（CoMatch + CCSSL）集成到CoMatch中，并引入了新的三元组度量学习损失（CoMatch + Triplet）。 CoMatch + Triplet 在 UCM 上表现出色，准确率为 93.2%，而 CoMatch 在 AID 上以 92.19% 的准确率领先。添加三元组损失可以在很早的学习阶段将来自嵌入空间中不同类别的样本更清晰地分离，从而能够更快地学习并通过很少的迭代获得最大的性能。这项工作中提出的各种半监督和自监督训练方法的探索揭示了这些方法的优点和局限性，增强了我们对其在遥感应用中的适用性的理解。