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 directly 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 perspectives 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 effects 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.