Target localization based on cross-view matching between UAV and satellite

作     者：Kan REN Lei DING Minjie WAN Guohua GU Qian CHEN Kan REN;Lei DING;Minjie WAN;Guohua GU;Qian CHEN

作者机构：Jiangsu Key Laboratory of Spectral Imaging and Intelligent SenseNanjing University of Science and TechnologyNanjing 210094China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2022年第35卷第9期

页      面：333-341页

核心收录：

学科分类：08[工学] 0825[工学-航空宇航科学与技术] 

基　　金：co-supported by the National Natural Science Foundations of China(Nos.62175111 and 62001234) 

主　　题：Cross-view image matching Satellite Target localization Template matching Unmanned Aerial Vehicle(UAV) 

摘      要：Matching remote sensing images taken by an unmanned aerial vehicle(UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UAV is determined. Its main challenge is the considerable differences in the visual content of remote sensing images acquired by satellites and UAVs, such as dramatic changes in viewpoint, unknown orientations, etc. Much of the previous work has focused on image matching of homologous data. To overcome the difficulties caused by the difference between these two data modes and maintain robustness in visual positioning, a quality-aware template matching method based on scale-adaptive deep convolutional features is proposed by deeply mining their common features. The template size feature map and the reference image feature map are first obtained. The two feature maps obtained are used to measure the similarity. Finally, a heat map representing the probability of matching is generated to determine the best match in the reference image. The method is applied to the latest UAV-based geolocation dataset(University-1652 dataset) and the real-scene campus data we collected with UAVs. The experimental results demonstrate the effectiveness and superiority of the method.