Drone Satellite Matching based on Multi-scale Local Pattern Network

Haoran Li, Quan Chen, Zhiwen Yang, Jiong Yin

Abstract: beginabstract In this technical report, we represent our solution for the ACMMM23 Multimedia Drone Satellite Matching Challenge. Our solution focuses on exploiting the feature aggregation strategy to develop a robust cross-view geo-localization system for Drone Satellite Matching. In particular, we propose an end-to-end framework named Multi-scale Local Pattern Network~(MLPN), which builds upon the LPN and incorporates a multi-scale aggregation block. LPN is employed to divide high-level features of different scales, and the multi-scale aggregation block, as the name implies, is utilized to aggregate the local features obtained by the division. Experiments show that MLPN can effectively match UAV images with satellite images and achieve a competitive accuracy on University160k. Additionally, in Multimedia Drone Satellite Matching Challenge, our solution achieves the fourth place. \endabstract