无人机（Unmanned Aerial Vehicle，UAV）因其轻便灵活、拍摄覆盖面广、成本较低等优势，在军事侦察、农业监测、城市规划与管理等领域发挥了重大作用。在这些应用中，无人机航拍因视野受限，无法得到完整目标区域的高清全景图像，因此图像拼接技术必不可少。近年来，深度学习的发展使得无人机航拍图像拼接技术再度受到关注。本文综述了近10年来无人机航拍图像拼接方法的研究进展。首先简介无人机航拍图像拼接方法的发展历程及主要流程。然后按图像预处理、图像配准、图像融合三大步骤详细说明无人机航拍图像拼接的传统方法，并对比每个步骤所采用方法的优缺点。接着阐述了基于深度学习的无人机航拍图像拼接方法，从基于深度学习的语义分割、图像配准、图像拼接框架这3个方面进行详细说明。随后梳理了常见无人机航拍图像拼接数据集与图像拼接性能评价指标，并列举了5项无人机航拍图像拼接技术的典型应用领域。最后指出无人机航拍图像拼接仍面临着多项技术挑战，并对未来工作进行了展望。

Unmanned Aerial Vehicle (UAV) plays an important role in military reconnaissance, agricultural monitoring, urban planning and management due to its advantages of light weight, flexibility, wide coverage and low cost. In these applications, due to the limited field of view of UAV aerial photography, it is impossible to obtain high-definition panoramic images of the complete target area, so image mosaic technology is essential. In recent years, the development of deep learning has made UAV aerial image mosaic technol-ogy attract attention again. In this paper, we review the research progress of UAV aerial image mosaic methods in the last 10 years. Firstly, the development process and main process of UAV aerial image mosaic method are introduced. Then the traditional methods of UAV aerial image mosaic are described in detail according to the three steps of image preprocessing, image registration and image fusion, and the advantages and disadvantages of the methods used in each step are compared. Then, the UAV aerial image mosaic method based on deep learning is described in detail from three aspects: semantic segmentation based on deep learning, image regis-tration, and image mosaic framework. Then, common UAV aerial image mosaic datasets and image mosaic performance evaluation indicators are sorted out, and five typical application fields of UAV aerial image mosaic technology are listed. Finally, it is pointed out that UAV aerial image Mosaic still faces many technical challenges, and the future work is prospected.