TCIE-ResNeXtGAN: infrared and water vapor satellite image fusion model using dual discriminator generative adversarial networks and ResNeXt for improving tropical cyclone intensity estimation accuracy

Jia-Xu Guo; Chang-Jiang Zhang

doi:10.1117/12.2661621

12 April 2023 TCIE-ResNeXtGAN: infrared and water vapor satellite image fusion model using dual discriminator generative adversarial networks and ResNeXt for improving tropical cyclone intensity estimation accuracy

Jia-Xu Guo, Chang-Jiang Zhang

Author Affiliations +

Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 125650F (2023) https://doi.org/10.1117/12.2661621
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

Abstract

Different characteristics of satellite images are reflected in different channels, so the monitoring and early warning of meteorological disasters based on satellite image data of a single channel may not achieve satisfactory results. The infrared channel satellite image reflects the ground and cloud top infrared radiation or the temperature distribution. The water vapor channel satellite image reflects the spatial distribution of water vapor in the upper atmosphere. The two channel satellite images reflect atmospheric characteristics from different wavebands. This paper proposes an infrared and water vapor channel satellite image fusion model (TCIE-ResNeXtGAN) based on dual discriminator generative adversarial network and ResNeXt to improve the accuracy of tropical cyclone (Tropical Cyclone, TC) intensity estimation. For this reason, we define the factors affecting TC intensity estimation, namely the brightness temperature gradient in satellite images, based on our previous work, and introduce it to the loss function in the proposed deep learning model to guide the training process of the model. In this way, the purpose of improving the estimation accuracy of TC intensity using fused satellite images is achieved. To demonstrate the effectiveness of the fusion results in this paper, we compare performance between the existing three models and our model. The experimental results show that the proposed image fusion method in this paper can preserve the infrared and water vapor dual-channel information to the greatest extent, while improving the estimation accuracy of TC intensity using fused satellite image.

Citation Download Citation

Jia-Xu Guo and Chang-Jiang Zhang "TCIE-ResNeXtGAN: infrared and water vapor satellite image fusion model using dual discriminator generative adversarial networks and ResNeXt for improving tropical cyclone intensity estimation accuracy", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 125650F (12 April 2023); https://doi.org/10.1117/12.2661621

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KEYWORDS

Image fusion

Satellite imaging

Satellites

Earth observing sensors

Infrared radiation

Infrared imaging

Performance modeling

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