Boosting Few-Shot Learning in Remote Sensing Leveraging an Auxiliary Generator through Contrastive Learning

Remote sensing technology has revolutionized the way we perceive and interact with our planet, enablingus to acquire invaluable information about our environment, natural resources, and ecosystems. Frommonitoring land use changes to studying climate patterns and disaster management, remote sensing hasbecome an indispensable tool for scientific research and practical applications. However, the effective application of remote sensing data often relies on the ability to classify andinterpret these vast and complex datasets. This presents a substantial challenge, as remote sensing datatypically exhibit high dimensionality, encompassing diverse terrain types, weather conditions, andimaging modalities. Furthermore, acquiring labeled data for supervised machine learning approaches isoften arduous, time-consuming, and expensive in remote sensing contexts. Few-shot learning emerges as a promising paradigm to address these issues. It seeks to develop modelscapable of learning from a limited amount of labeled data, which is a common scenario in remote sensingapplications due to the cost and effort associated with ground truth data collection. Few-shot learning isespecially valuable in cases where we need to recognize previously unseen classes or adapt quickly tochanging conditions, such as tracking forest fires or monitoring urban development. Despite its potential, few-shot learning in remote sensing faces significant hurdles, such as inadequatemodel generalization, limited data diversity, and challenges in feature extraction from complex datasources. In recent years, several advancements have been made in remote sensing image analysis, including the integration of deep learning techniques, but there remains a critical need for improving few- shot learning capabilities in this domain. This paper introduces a novel approach to address the limitations of few-shot learning in remote sensing. We propose the incorporation of an auxiliary generator powered by contrastive learning, a technique thathas demonstrated success in diverse applications, from natural language processing to computer vision. This auxiliary generator, working in conjunction with our few-shot learning model, enhances the ability todiscriminate between different classes and adapt to new remote sensing scenarios with limited labeleddata