  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}.
