The Future of Remote Sensing: Few-Shot Learning and Explainable AI | HackerNoon

Table of Links

1. Abstract and Introduction
2. Backgrounds
3. Type of remote sensing sensor data
4. Benchmark remote sensing datasets for evaluating learning models
5. Evaluation metrics for few-shot remote sensing
6. Recent few-shot learning techniques in remote sensing
7. Few-shot based object detection and segmentation in remote sensing
8. Discussions
9. Numerical experimentation of few-shot classification on UAV-based dataset
10. Explainable AI (XAI) in Remote Sensing
11. Conclusions and Future Directions
12. Acknowledgements, Declarations, and References

11 Conclusions and Future Directions

In this comprehensive review, we provided a comprehensive analysis of recent few-shot learning techniques for remote sensing across various data types and platforms. Compared to previous reviews [9], we expanded the scope to include UAV-based datasets. Our quantitative experiments demonstrated the potential of few-shot methods on various remote sensing datasets. We also emphasized the growing importance of XAI to increase model transparency and trustworthiness.

While progress has been made, ample opportunities remain to advance few-shot learning for remote sensing. Future research could explore tailored few-shot approaches for UAV data that account for unique image characteristics and onboard computational constraints. Vision transformer architectures could also be investigated for few-shot classification of very high-resolution remote sensing data. A key challenge is reducing the performance discrepancy between aerial and satellite platforms. Developing flexible techniques that handle diverse data effectively is an open problem that warrants further investigation.

On the XAI front, further work is needed to address issues unique to remote sensing like scarce labeled data, complex earth systems, and integrating domain knowledge into models. Techniques tailored for few-shot learning specifically could benefit from more research into explainable feature extraction and decision making. Explainability methods that provide feature-level and decision-level transparency without sacrificing too much accuracy or efficiency are needed. There is also potential to apply few-shot learning and XAI to new remote sensing problems like object detection, semantic segmentation, and anomaly monitoring.

To end, few-shot learning shows increasing promise for efficient and accurate analysis of remote sensing data at scale. Integrating XAI can further improve model transparency, trust, and adoption by providing humanunderstandable explanations. While progress has been made, ample challenges and opportunities remain to realize the full potential of few-shot learning and XAI across the diverse and rapidly evolving remote sensing application landscape. Advances in these interconnected fields can pave the way for remote sensing systems that learn quickly from limited data while remaining transparent, accountable, and fair.

Acknowledgements

This research/project is supported by the Civil Aviation Authority of Singapore and Nanyang Technological University, Singapore under their collaboration in the Air Traffic Management Research Institute. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Civil Aviation Authority of Singapore.

Declarations

The authors have no conflicts of interest to declare.

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