Advancing Remote Sensing with Few-Shot Learning: A Comprehensive Review of Methods, Challenges, and Future Directions

Abstract

In this review, the details and developments of few-shot learning (FSL) techniques in different remote sensing (RS) studies including change monitoring, disaster management, urban monitoring, and agriculture are discussed in detail. Furthermore, a categorization is made by dividing FSL methods into three categories (metric-based, optimization-based, and transfer learning approaches) and considering hybrid approaches. Special attention is given to episodic training and meta-learning approaches that provide rapid adaptation to new classes with minimal examples. Furthermore, the integration of explainable artificial intelligence (XAI) and its real-time application capabilities are discussed. Important issues such as domain shift, class imbalance, and high dimensionality are discussed. Recent refinements such as task-level learning, data augmentation, and multimodal integration are examined. Finally, a coherent framework is suggested for further studies and practical FSL applications in the context of RS. As a result, it provides a more comprehensive perspective than previous reviews. This review aimed to guide future research in the integration of FSL with RS applications by analyzing the existing literature and pointing out important research gaps.