Deep learning fusion of multi-channel satellite images improves the accuracy of tropical cyclone intensity estimation

Different channels of satellite images can provide different tropical cyclone (TC) characteristics, while infrared (IR) channel satellite images reflect cloud top IR radiation and temperature distribution. The satellite images of the water vapor (WV) channel reflect the spatial distribution of WV in the atmosphere. Passive microwave (PMW) signals can penetrate clouds, providing information on eye wall structure and precipitation distribution. Therefore, compared to single channel data, multi-channel data can better provide feature information closely related to TC intensity. This article proposes a deep learning based fusion model for IR, WV, and PMW satellite images, which improves its accuracy in TC intensity estimation while ensuring the quality of the generated fusion images. We design a dual encoding and decoding structure and design different loss functions for each encoding and decoding model according to the factors affecting TC intensity estimation to guide the training process of the model. In addition, we improved the L1-norm fusion strategy to further improve its fusion effect. To demonstrate the effectiveness of the fusion images generated by our model, we compare its performance with four other fusion models. The comparative experimental results show that our proposed model not only effectively retains the information of multi-channel satellite images, but also improves the estimation accuracy of TC intensity by fusion satellite images.