Imaging systems with different imaging sensors are widely applied to surveillance field, military field, and medicine field. Particularly, infrared imaging sensors can acquire thermal radiations emitted by different objects but lack textural details, and visible imaging sensors can capture abundant textural information but suffer from loss of scene information under poor weather conditions. The fusion of infrared and visible images can synthesize a new image with complementary information of the source images. In this paper, we present a deep learning method with encoder–decoder architecture for infrared and visible image fusion. Firstly, multiscale channel attention blocks are introduced to extract features at different scales, which can preserve more meaningful information and enhance the important information. Secondly, we utilize the improved fusion strategy based on visual saliency to fuse feature maps. Lastly, the fusion result is restored via reconstruction network. In comparison with other state‐of‐the‐art approaches, our experimental results achieve appealing performance on visual effects and objective assessments.

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As the aviation industry has entered a critical period of development, the demand for Automatic Dependent Surveillance Broadcast (ADS-B) technology is becoming increasingly urgent. Real-time detection of aviation wind field information and the early warning of wind field shear by atmospheric sounding system are two important factors related to the safe operation of aviation and airport. According to the advantages of ADS-B and Mode S data, this paper uses the Meteo-Particle (MP) model proposed by Sun et al., in their previous research to retrieve high-altitude wind field. Comparing the precision and accuracy of wind field retrieved results, and the optimization parameters of MP model suitable for meteorological model are further studied. To solve the problem of incomplete wind field coverage obtained by retrieval, an extrapolation algorithm of wind field is proposed. The results show that: (1) a comprehensive evaluation index is introduced, which can more effectively evaluate the comprehensive difference of wind field retrieval results in wind speed and direction. (2) The adaptability results of MP model in different periods and altitudes provide some reference for the research of other scholars. (3) The new parameter setting can improve the accuracy of the retrieved results, and the appropriate extrapolation of wind field fills in the blank part of aviation and meteorology.