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基于多视角低空遥感图像的船舶目标关联

刘云鹤 姜智卓 刘瑜 孙显 何友

航空学报2026,Vol.47Issue(10):63-77,15.
航空学报2026,Vol.47Issue(10):63-77,15.DOI:10.7527/S1000-6893.2026.33060

基于多视角低空遥感图像的船舶目标关联

Vessel target association based on multi-view low-altitude remote sensing images

刘云鹤 1姜智卓 2刘瑜 3孙显 4何友3

作者信息

  • 1. 清华大学 深圳国际研究生院,深圳 518000
  • 2. 南开大学 计算机学院,天津 300071
  • 3. 清华大学 电子工程系,北京 100084
  • 4. 中国科学院 空天信息创新研究院,北京 100094||中国科学院 网络信息体系技术重点实验室,北京 100190
  • 折叠

摘要

Abstract

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.

关键词

低空遥感/船舶目标关联/相关性建模/特征增强/特征融合

Key words

low-altitude remote sensing/vessel target association/correlation modeling/feature enhancement/feature fusion

分类

航空航天

引用本文复制引用

刘云鹤,姜智卓,刘瑜,孙显,何友..基于多视角低空遥感图像的船舶目标关联[J].航空学报,2026,47(10):63-77,15.

基金项目

国家自然科学基金(62401335) National Natural Science Foundation of China(62401335) (62401335)

航空学报

1000-6893

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