Unlocking the capabilities of explainable fewshot learning in remote sensing

Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, fewshot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies. In this review, we provide an up to date overview of both existing and newly proposed fewshot classification techniques, along with appropriate datasets that are used for both satellite based and UAV based data. Our systematic approach demonstrates that fewshot learning can effectively adapt to the broader and more diverse perspectives that UAVbased platforms can provide. We also evaluate some SOTA fewshot approaches on a UAV disaster scene classification dataset, yielding promising results. We emphasize the importance of integrating XAI techniques like attention maps and prototype analysis to increase the transparency, accountability, and trustworthiness of fewshot models for remote sensing. Key challenges and future research directions are identified, including tailored fewshot methods for UAVs, extending to unseen tasks like segmentation, and developing optimized XAI techniques suited for fewshot remote sensing problems. This review aims to provide researchers and practitioners with an improved understanding of fewshot learnings capabilities and limitations in remote sensing, while highlighting open problems to guide future progress in efficient, reliable, and interpretable fewshot methods.

WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images

Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 15 times in terms of classification accuracy and 38.3% in terms of computing efficiency as measured by Floating Point Operations per second (FLOPs). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency. Our code is available at \url{https://github.com/TanmDL/WATT-EffNet}.