Details

Autor(en) / Beteiligte

• Zhang, Chang-Jiang
• Guo, Jia-Xu
• Ma, Lei-Ming
• Lu, Xiao-Qin
• Liu, Wen-Cai

Titel

TCCL-DenseFuse: Infrared and Water Vapor Satellite Image Fusion Model Using Deep Learning

Ist Teil von

• IEEE journal of selected topics in applied earth observations and remote sensing, 2023-01, Vol.16, p.1-25

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• This paper proposes an infrared and water vapor channel satellite image fusion model (TCCL-DenseFuse) based on DenseNet. The infrared channel satellite image reflects the ground and cloud top infrared radiation or the distribution of temperature, and the water vapor channel satellite image reflects the spatial distribution of water vapor in the upper atmosphere. Studies have shown that infrared brightness temperature gradient and water vapor transport are closely related to TC generation and evolution. In order to facilitate the fusion image obtained by the proposed fusion model to have a positive effect on tropical cyclone monitoring and warning, the brightness temperature gradient and multi-scale structural similarity in the satellite image are used to construct loss function of the proposed TCCL-DenseFuse model. Quality of the fused images are evaluated by seven objective quantitative indicators. In order to further verify the real application value of the proposed TCCL-DenseFuse model, fused images are also used to TC center location. Experimental results show that the proposed TCCL-DenseFuse fused satellite image not only contains rich information from both infrared and water vapor channels but also improves the accuracy of TC center positioning. The comprehensive performance of the proposed fusion model has certain advantages compared with similar fusion methods and can provide a reference for typhoon prevention and disaster mitigation.