Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation

TL;DR

This paper presents a new semi-supervised active learning method combining contrastive learning and uncertainty estimation to improve label efficiency and accuracy in Sentinel-2 satellite imagery analysis.

Contribution

It introduces a novel approach integrating contrastive learning with uncertainty estimation for active learning in satellite image classification, especially under class imbalance.

Findings

Outperforms existing methods in accuracy and label efficiency

Effective in both balanced and unbalanced class scenarios

Reduces labeling effort significantly while maintaining high performance

Abstract

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.