基于多视角低空遥感图像的船舶目标关联OA

Vessel target association under low-altitude remote-sensing scenarios is a crucial component supporting the development of maritime monitoring and intelligent perception systems.However,most existing approaches di-rectly migrate pedestrian or vehicle re-identification algorithms,which fail to effectively handle the unique challenges of vessel imagery-particularly the large intra-class variations and local information loss caused by the diverse imaging per-spectives of UAV-based low-altitude imaging platforms.These issues often lead to outlier samples within the same vessel identity,significantly degrading association accuracy.To overcome these limitations,this paper proposes a Multi-scale Correlation-aware Transformer network(MCFormer)for vessel target association.Unlike conventional methods that learn from isolated features of single images,MCFormer performs explicit global and local correlation modeling across multi-scale image collections,leveraging inter-image complementary information to suppress the ef-fects of intra-identity variance and partial occlusion.Specifically,a Global Correlation Module(GCM)constructs a comprehensive inter-image similarity matrix to achieve explicit global correlation modeling through consistency-based feature aggregation,while a Local Correlation Module(LCM)builds a dynamically updated memory bank to mine and align positive local features,capturing fine-grained contextual correlations.Experiments conducted on four publicly available real-world datasets demonstrate that the proposed method consistently outperforms mainstream method in performance metrics related to target association accuracy,verifying its effectiveness,robustness,and engineering potential.