  Given the destructive impacts of tropical cyclones, it is critical to have a
reliable system for cyclone intensity detection. Various techniques are
available for this purpose, each with differing levels of accuracy. In this
paper, we introduce two ensemble-based models based on AlexNet architecture to
estimate tropical cyclone intensity using visible satellite images. The first
model, trained on the entire dataset, is called the global AlexNet model. The
second model is a distributed version of AlexNet in which multiple AlexNets are
trained separately on subsets of the training data categorized according to the
Saffir-Simpson wind speed scale prescribed by the meterologists. We evaluated
the performance of both models against a deep learning benchmark model called
\textit{Deepti} using a publicly available cyclone image dataset. Results
indicate that both the global model (with a root mean square error (RMSE) of
9.03 knots) and the distributed model (with a RMSE of 9.3 knots) outperform the
benchmark model (with a RMSE of 13.62 knots). We provide a thorough discussion
of our solution approach, including an explanantion of the AlexNet's
performance using gradient class activation maps (grad-CAM). Our proposed
solution strategy allows future experimentation with various deep learning
models in both single and multi-channel settings.
