Intensity classification for tropical cyclone using feature fusion of infrared satellite image

Yu Wang; Chang-Jiang Zhang

doi:10.1117/12.2662183

12 April 2023 Intensity classification for tropical cyclone using feature fusion of infrared satellite image

Yu Wang, Chang-Jiang Zhang

Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 125651A (2023) https://doi.org/10.1117/12.2662183
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

Abstract

Accurate estimation of tropical cyclone (TC) intensity, as an important task for meteorological hazard monitoring and warning, is often regarded as a problem of intensity grade classification or intensity regression. Good classification accuracy of TC intensity is of great significance to improve the accuracy of TC intensity estimation. Based on the infrared brightness temperature data of the Northwest Pacific Basin, a TC intensity classification method based on feature fusion of infrared satellite images is proposed in this paper. The proposed method divides infrared satellite images into three categories according to intensity grades of TCs: TS+STS, STY, VSTY+ViolentTY. Firstly, the features of the middle layer of Xception network which is one of deep convolutionary neural network models are extracted. Then, after 1×1 convolution and pooling, they are fused with the features of the output of the full connection layer. Finally, the fused features are sent into the Softmax classifier to determine the category. In this paper, by fusing the information from different convolutional layers, the information complementarity of global and local features can be realized. The results show that the proposed model has the best performance after the fusion of the features output from the seventh residual module in middle flow and the output features of the full connection layer. The classification accuracy of TC intensity grades is improved to 80.99%, which is 1.5% higher than the original Xception network. The proposed method can be applied to the task of TC intensity estimation.

Citation Download Citation

Yu Wang and Chang-Jiang Zhang "Intensity classification for tropical cyclone using feature fusion of infrared satellite image", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 125651A (12 April 2023); https://doi.org/10.1117/12.2662183

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KEYWORDS

Satellites

Satellite imaging

Earth observing sensors

Image fusion

Infrared radiation

Infrared imaging

Data modeling

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