基于多视角多监督网络的无人机图像定位方法

Unmanned aerial vehicle image localization method based on multi-view and multi-supervision network

针对现有跨视角图像匹配算法精度低的问题,提出了一种基于多视角多监督网络(MMNet)的无人机(UAV)定位方法。首先,所提方法融合卫星视角和UAV视角,在统一的网络架构下学习全局和局部特征并以多监督方式训练分类网络并执行度量任务。具体来说,MMNet主要采用了重加权正则化三元组损失(RRT)学习全局特征,该损失利用重加权和距离正则化加权策略来解决多视角样本不平衡以及特征空间结构紊乱的问题。同时,为了关注目标地点中心建筑的上下文信息,MMNet对特征图进行方形环切割来获取局部特征。然后,分别用交叉熵损失和RRT执行分类和度量任务。最终,使用加权策略聚合全局和局部特征来表征目标地点图像。通过在当前流行的UAV数据集University-1652上进行实验,可知MMNet在UAV定位任务的召回率Recall@1 (R@1)及平均精准率(AP)上分别达到83.97%和86.96%。实验结果表明,相较于LCM、SFPN等方法,MMNet显著提升了跨视角图像的匹配精度,进而增强了UAV图像定位的实用性。

Aiming at the problem of low accuracy of the existing cross-view image matching algorithms, an Unmanned Aerial Vehicle(UAV) image localization method based on Multi-view and Multi-supervision Network(MMNet) was proposed. Firstly, in the proposed method, satellite perspective and UAV perspective were integrated, global and local features were learnt under a unified network architecture, then classification network was trained and metric tasks were performed in multi-supervision way. Specifically, the Reweighted Regularization Triplet loss(RRT) was mainly used by MMNet to learn global features. In this loss, the reweighting and distance regularization strategies were to solve the problems of imbalance of multi-view samples and structure disorder of the feature space. Simultaneously, in order to pay attention to the context information of the central building in target location, the local features were obtained by MMNet via square ring cutting. After that, the cross entropy loss and RRT were used to perform classification and metric tasks respectively.Finally, the global and local features were aggregated by using a weighted strategy to present target location images. MMNet achieved Recall@1(R@1) of 83. 97% and Average Precision(AP) of 86. 96% in UAV localization tasks on the currently popular UAV dataset University-1652. Experimental results show that MMNet significantly improves the accuracy of cross-view image matching, and then enhances the practicability of UAV image localization compared with LCM(cross-view Matching based on Location Classification), SFPN(Salient Feature Partition Network) and other methods.