DeepTrCy: Life Stage Identification of Satellite Tropical Cyclone Images

Satellite imagery with multiple spectral bands plays a crucial role in the analysis and prediction of atmospheric phenomena. Tracking tropical cyclones, i.e typhoons, are easily monitored by infrared bands with predefined typhoon life stages. In the life cycle of such typhoons, they typically exhibit formation of a few clouds to fully developed spiral clouds with a clear eye. The life stage is classified by analyzing the maximum sustained winds at a distance from the center of the eye. The lowest pressure is also used to characterize the magnitude of typhoons. Moreover, apparent typhoon shapes can be roughly classified into different life stages according to experts. However, this limits other applications due to auto-operational tasks. Therefore, this paper proposes a Deep Learning based satellite image classification for identifying the life stage of tropical cyclones, namely, DeepTrCy. Due to the limited training on past typhoon images, applying data augmentation by introducing a fluid-like noise, i.e., Perlin noise, is firstly proposed. Using different DL models by introducing different augmentation techniques like Perlin noise levels, and different input data like optical flow, results are compared to that of CNN and transformer architectures. The proposed DeepTrCy has demonstrated high accuracy on identifying tropical cyclone stages from the several experiments.