The article presents an in-depth review of few-shot learning applications in remote sensing, marking significant advancements in UAV datasets. It highlights various techniques, showcases quantitative experiments indicating promising results, and underscores the critical role of Explainable AI (XAI) in enhancing model transparency. The discussion points towards future avenues in few-shot learning, specifically for UAV data, and stresses the importance of addressing challenges regarding heterogeneous data sources and model interpretability. The call for more research into tailored methodologies for unique datasets and improved explainability techniques for better decision-making is prominent.
In our review, we analyzed few-shot learning techniques for remote sensing and emphasized the need for XAI to enhance model transparency and trust.
Future research should tailor few-shot approaches for UAV data, focusing on the unique image characteristics and onboard computational constraints.
Investigating vision transformer architectures could significantly improve few-shot classification for high-resolution remote sensing data.
Addressing performance discrepancies between aerial and satellite platforms is crucial, as is developing flexible techniques that manage diverse datasets.
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