In this paper, we introduce a novel method designed to enhance label efficiency in satellite imagery analysis by integrating semi-supervised learning (SSL) with active learning strategies. Our approach utilizes contrastive learning together with uncertainty estimations via Monte Carlo Dropout (MC Dropout), with a particular focus on Sentinel-2 imagery analyzed using the Eurosat dataset. We explore the effectiveness of our method in scenarios featuring both balanced and unbalanced class distributions. Our results show that the proposed method performs better than several other popular methods in this field, enabling significant savings in labeling effort while maintaining high classification accuracy. These findings highlight the potential of our approach to facilitate scalable and cost-effective satellite image analysis, particularly advantageous for extensive environmental monitoring and land use classification tasks.

翻译：本文提出一种新颖方法，旨在通过将半监督学习与主动学习策略相结合，提升卫星影像分析中的标注效率。我们的方法利用对比学习以及通过蒙特卡洛Dropout进行的不确定性估计，并特别关注使用Eurosat数据集分析的Sentinel-2影像。我们探究了该方法在类别分布平衡与不平衡场景下的有效性。结果表明，所提方法优于该领域其他几种主流方法，能够在保持高分类精度的同时显著节省标注工作量。这些发现凸显了本方法在推动可扩展、高性价比的卫星影像分析方面的潜力，尤其适用于大规模环境监测与土地利用分类任务。