Few-Shot Remote Sensing Image Classification with Meta-Learning

The performance of machine learning models relies on the quality, quantity, and diversity of annotated remote sensing datasets. However, the expensive effort required to annotate samples from diverse locations around the globe, coupled with the need for higher computation, often leads to models that are less generalizable across different regions. This paper explores the use of few-shot learning with meta-learning to improve the generalization capability of deep learning models on remote sensing image classification problems with limited annotated samples. The experiments show that metric-based meta-learners, such as prototypical and matching networks, provide comparable performance to more complex optimization-based meta-learning approaches like model-agnostic meta-learning and its variations. Few-shot learning with meta-learning can unlock greater generalization capabilities in machine learning models, thereby significantly impacting various remote sensing applications.