Deep Learning Approach for Tropical Cyclones Classification Based on C-Band Sentinel-1 SAR Images

Since the first method proposed by Dvorak in the 70's, Tropical Cyclone (TC) monitoring strategy has been routinely improved for both operational analysis and forecasting of tropical cyclone intensity. However, one of the most widely used techniques for operational TC intensity analysis remains largely subjective, dependent on analyst training and lacking in incorporation of imagery beyond visible and infrared window frequencies. Using high-resolution data from the Sentinel-1 Synthetic Aperture Radar (SAR) mission, this study explores a Deep Learning approach to detect the eye-center of tropical cyclones and to estimate their intensity based on topology patterns. Moreover, we apply Gradient-based Class Activation Maps to better understand the characteristics of this learning based method. Experimental results demonstrate that the suggested approach not only recognizes TCs effectively, but also reveals potential to locate their centers accurately and compete in performance with existing subjective and automated intensity estimation techniques.