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

Abstract

This article 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 tropical cyclone (TC) generation and evolution. In order to facilitate the fusion image obtained by the proposed fusion model to have a positive effect on TC monitoring and warning, the brightness temperature gradient and multiscale structural similarity in the satellite image are used to construct loss function of the proposed TCCL-DenseFuse model. The quality of the fused images is 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. Index Terms-Deep learning, infrared image, satellite image fusion, tropical cyclone (TC) center positioning, water vapor image. I. INTRODUCTIONS ATELLITE image data commonly used in meteorological services are mainly derived from polar-orbiting and geostationary satellites. Because of their good temporal resolution and wide coverage, geostationary satellites are widely used in meteorological disaster monitoring and forecasting. Geostationary