Tropical cyclone detection by combining wavelet transform with deep learning using infrared satellite images

ABSTRACT

A tropical cyclone (TC) is a meteorological disaster that occurs over tropical or subtropical oceans. Automatic TC identification from a satellite image is essential to the subsequent automatic intelligent determination of TC positioning and intensity. Few recent studies have examined the automatic detection of TCs from satellite images. In this paper, wavelet (W) transformation, efficient (E) channel attention mechanism and YOLOX (Y) are combined to construct a deep learning model for TC detection, referred to as WEY-TCNet. WEY-TCNet automatically detects TCs from infrared satellite images. To improve the detection accuracy, a discrete wavelet transform is first used to decompose the infrared satellite image, after which the horizontal high-frequency components, including the TC rainband structure and inner core area, are extracted and fused with the original image. This step enhances the TC structure and brightness temperature gradient characterization. The fused image is then used as the input to WEY-TCNet. The ECANet channel attention mechanism was added to the original YOLOX-S network structure to focus on the key features of TC, such as structure and brightness temperature while suppressing invalid features and noise. This study constructed a satellite image dataset based on the infrared images of the Chinese Fengyun2D geostationary satellite. The dataset contains infrared satellite images of TCs in various life cycle stages, from generation to extinction. Our experiments showed that the WEY-TCNet model proposed in this paper achieves 91.49% detection mAP on the TC dataset, compared with the 86.69% detection mAP of the original YOLOX-S model, significantly improving it.

KEYWORDS:

• Tropical cyclone detection
• infrared satellite imagery
• YOLOX-S
• efficient channel attention
• discrete wavelet transform

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Acknowledgements

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported in part by National Natural Science Foundation of China (42075140 and 41575046), and in part by Zhejiang Province Public Welfare Technology Application Research Project, China under Grant LGF20D050004.