一种面向无人机绝对定位的卫星基准影像检索方法  

A Satellite Reference Image Retrieval Method for Unmanned Aerial Vehicle Absolute Positioning

作　　者：秦剑琪 蓝朝桢[1] 崔志祥 张永显 王岩[1] QIN Jianqi;LAN Chaozhen;CUI Zhixiang;ZHANG Yongxian;WANG Yan(Institute of Geospatial Information,Information Engineering University,Zhengzhou 450001,China;2263611 Troops,Korla 841001,China;3331682 Troops,Lanzhou 730020,China)

机构地区：[1]信息工程大学地理空间信息学院,河南郑州450001 [2]263611部队,新疆库尔勒841001 [3]331682部队,甘肃兰州730020

出　　处：《武汉大学学报（信息科学版）》2023年第3期368-376,共9页Geomatics and Information Science of Wuhan University

基　　金：中原科技创新领军人才计划(194200510023)。

摘　　要：针对全球导航卫星系统(global navigation satellite system,GNSS)拒止环境下大范围无人机视觉绝对定位问题,提出了一种聚合深度学习特征的卫星基准影像检索方法。首先,利用预训练的深度学习模型提取无人机与卫星基准影像的局部卷积特征;然后,对局部特征描述符进行聚合,生成影像全局表达;最后,利用影像全局特征进行相似性检索,并采用检索结果精匹配重排序的后处理方法,进一步提高检索准确率。设计了一个新的面向无人机绝对定位的卫星基准影像数据集并进行实验,结果表明,使用所提方法检索无人机影像适配区域的卫星基准影像的准确率达76.07%,可为后续基于视觉的无人机绝对定位提供参考。Objectives: In recent years, unmanned aerial vehicle has been widely used and their navigation and positioning rely heavily onglobal navigation satellite system(GNSS). In the case of GNSS rejection, visual navigation and positioning technology can compensate for this problem,but the technique will also fail to adapt if the approximate location of unmanned aerial vehicle cannot be estimated.To cope with this problem,we propose a reference satellite image retrieval method that aggregates deep learning features to determine the range of unmanned aerial vehicle image adaption region,which can provide reference for the following unmanned aerial vehicle absolute positioning. Methods: Firstly,the pre-trained deep learning model is used to extract local convolution features of unmanned aerial vehicle images and satellite images.Secondly,the local aggregation descriptor vector is used to generate the global expression of the images. Finally,the global feature of the image is used to perform similarity retrieval and post-processing method of matching precisely and reranking the retrieval results is used, which further improves the retrieval accuracy.A new satellite reference image data set for absolute positioning of unmanned aerial vehicle is designed and tested.Results:When the queried unmanned aerial vehicle image is similar to the satellite image season in the database,the accuracy of the top 50 candidate images can reach 87.50% using the proposed features for retrieval.Combined with the refined matching re-ranking, the accuracy of the first candidate image can reach up to 76.07%, which satisfies general navigation and positioning applications.Conclusions: Although the global descriptor based on deep feature aggregation can effectively represent the images of texture-obvious regions,it is not strong in representing the images of texture-lacking regions and its overlap range between images is high when retrieval is performed.Therefore, the efficiency problem of retrieval and the image feature representation of texture-s

关 键 词：全球导航卫星系统拒止 无人机定位 基准影像 影像检索 

分 类 号：P237[天文地球—摄影测量与遥感]