The purposes are to explore the video image stitching technique of Unmanned Aerial Vehicles (UAVs), expand the application of image registration algorithms and new sensing equipment in video image stitching, and improve the development of video remote sensing image processing. First, how to remotely stitch UAV video images is analyzed. Second, the Scale Invariant Feature Transform (SIFT) algorithm is improved by re-dividing the pixel region and incorporating the vector correlation coefficient. Then, the matching results of the improved SIFT algorithm are compared with those of the traditional SIFT algorithm and Speed Up Robust Feature algorithm. Second, the Random Sample Consensus algorithm is introduced to match the video images accurately, and the matching accuracy of the converted images is verified. Finally, the improved image registration algorithm, the homography matrix estimation model based on convolutional neural network, and the conformation equation of the video sensor are combined to complete the video image stitching of UAVs. Also, the stitching quality of the video image is analyzed. The results show that the improved SIFT algorithm is better than the traditional SIFT algorithm in terms of correct matching and matching time. The visually transformed finely-matched image has a higher matching accuracy rate, and the combination of the two registration algorithms eliminates mismatch points effectively. Compared with the traditional stitching method, the video image stitching method proposed in this study has a higher structural similarity index and edge difference spectrum index, which is feasible and effective. The combination of image registration algorithm with new sensors and deep learning has great application potential in video image stitching.

目的是探索无人机的视频图像拼接技术，扩大图像配准算法和新型传感设备在视频图像拼接中的应用，促进视频遥感图像处理的发展。首先，分析了如何远程缝合无人机视频图像。其次，通过重新划分像素区域并合并矢量相关系数，改进了尺度不变特征变换（SIFT）算法。然后，将改进的SIFT算法的匹配结果与传统的SIFT算法和加速鲁棒特征算法的匹配结果进行比较。其次，引入随机样本共识算法对视频图像进行精确匹配，并验证了转换后图像的匹配精度。最后，结合改进的图像配准算法，基于卷积神经网络的单应矩阵估计模型和视频传感器的构造方程，完成无人机的视频拼接。另外，分析视频图像的拼接质量。结果表明，改进的SIFT算法在正确匹配和匹配时间方面优于传统的SIFT算法。经视觉变换的精细匹配图像具有更高的匹配准确率，并且两种配准算法的结合有效地消除了失配点。与传统的拼接方法相比，本研究提出的视频图像拼接方法具有更高的结构相似度指数和边缘差分频谱指数，既可行又有效。