Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation
This work addresses scalable and cost-effective satellite image analysis for environmental monitoring and land use classification, though it appears incremental as it combines existing techniques.
The paper tackles label efficiency in satellite imagery analysis by integrating semi-supervised learning with active learning using contrastive learning and Monte Carlo Dropout uncertainty estimation on Sentinel-2 imagery, achieving better performance than other methods with significant labeling effort savings while maintaining high classification accuracy.
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.