21 citations · 25 across the 4 of their papers we have counts for
4 papers
Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
The rising incidence of natural and human-induced disasters necessitates robust visual recognition systems capable of operating under limited labeled data conditions. However, disa…
Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches
Gao Yu Lee, Jinkuan Chen, Tanmoy Dam +3
High-quality images are crucial in remote sensing and UAV applications, but atmospheric haze can severely degrade image quality, making image dehazing a critical research area. Sin…
Unlocking the capabilities of explainable fewshot learning in remote sensing
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large am…
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2
Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts.…