A Multiscale and Multilayer Feature Extraction Network With Dual Attention for Tropical Cyclone Intensity Estimation

A tropical cyclone (TC) is a type of catastrophic weather encountered in the tropical or subtropical ocean, and it is of great significance to accurately estimate its intensity. Many estimation methods based on statistics have been proposed, but these methods have obvious problems, such as poor robustness and low accuracy. Therefore, in this study, a TC intensity estimation method is proposed based on satellite image data using the Xception network as a backbone. The main idea of the proposed method is to estimate the TC maximum wind speed by image feature extraction. First, a Laplacian pyramid image fusion method for the infrared (IR) and water vapor (WV) channels of satellite images is adopted to enhance the total amount of information in the basic input data of the model. Second, an optimization strategy for the depth and width of the Xception network model is proposed with the objective of reducing parameter redundancy and improving the estimation accuracy. Third, a multiscale feature extraction module and a multilayer feature fusion module are designed to realize the fusion of different features. In addition, a dual attention module is introduced to allow the model to focus on the key regions of cyclone images. Finally, the proposed network is evaluated on the HURSAT, FY-2, and Gridsat datasets. The results show that, on average, the maximum cyclone wind speed estimation errors, the mean absolute error (MAE), and root mean square error (RMSE), are 8.0% and 11.4% lower than the state-of-the-art models on the three datasets.