Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline

doi:10.3390/rs17152653

	Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline
	Shang, Linzhi 1; Min, Chen 2; Wang, Juan 3; Xiao, Liang 1; Zhao, Dawei 1; Nie, Yiming 1
	2025-07-31
发表期刊	REMOTE SENSING
卷号	17 期号:15 页码:21
摘要	Vehicle re-identification (Re-ID) is a critical computer vision task that aims to match the same vehicle across spatially distributed cameras, especially in the context of remote sensing imagery. While prior research has primarily focused on Re-ID using remote sensing images captured from similar, typically elevated viewpoints, these settings do not fully reflect complex aerial-ground collaborative remote sensing scenarios. In this work, we introduce a novel and challenging task: aerial-ground cross-view vehicle Re-ID, which involves retrieving vehicles in ground-view image galleries using query images captured from aerial (top-down) perspectives. This task is increasingly relevant due to the integration of drone-based surveillance and ground-level monitoring in multi-source remote sensing systems, yet it poses substantial challenges due to significant appearance variations between aerial and ground views. To support this task, we present AGID (Aerial-Ground Vehicle Re-Identification), the first benchmark dataset specifically designed for aerial-ground cross-view vehicle Re-ID. AGID comprises 20,785 remote sensing images of 834 vehicle identities, collected using drones and fixed ground cameras. We further propose a novel method, Enhanced Self-Correlation Feature Computation (ESFC), which enhances spatial relationships between semantically similar regions and incorporates shape information to improve feature discrimination. Extensive experiments on the AGID dataset and three widely used vehicle Re-ID benchmarks validate the effectiveness of our method, which achieves a Rank-1 accuracy of 69.0% on AGID, surpassing state-of-the-art approaches by 2.1%.
关键词	aerial-ground cross-view remote sensing vehicle re-identification
DOI	10.3390/rs17152653
收录类别	SCI
语种	英语
资助项目	Defense Innovation Institution
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001549662100001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41749
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Dawei
作者单位	1.Def Innovat Inst, Beijing 100071, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China
推荐引用方式 GB/T 7714	Shang, Linzhi,Min, Chen,Wang, Juan,et al. Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline[J]. REMOTE SENSING,2025,17(15):21.
APA	Shang, Linzhi,Min, Chen,Wang, Juan,Xiao, Liang,Zhao, Dawei,&Nie, Yiming.(2025).Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline.REMOTE SENSING,17(15),21.
MLA	Shang, Linzhi,et al."Aerial-Ground Cross-View Vehicle Re-Identification: A Benchmark Dataset and Baseline".REMOTE SENSING 17.15(2025):21.