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WANG Junyu, SUN Hao, HUANG Qihao, et al. SAR image target interpretation based on vision-language model: a survey[J]. Journal of Radars, in press. doi: 10.12000/JR25256
Citation: WANG Junyu, SUN Hao, HUANG Qihao, et al. SAR image target interpretation based on vision-language model: a survey[J]. Journal of Radars, in press. doi: 10.12000/JR25256
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SAR Image Target Interpretation Based on Vision-language Model: A Survey

DOI: 10.12000/JR25256 CSTR: 32380.14.JR25256

  • State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The Joint Funds of the National Natural Science Foundation of China (U24B20189)
More Information
  • Corresponding author: SUN Hao, sunhao@nudt.edu.cn
  • Received Date: 2025-12-01
  • Rev Recd Date: 2026-01-31
    • Available Online: 2026-02-06
    Abstract
  • Synthetic Aperture Radar (SAR) is widely used in military and civilian applications, with intelligent target interpretation of SAR images being a crucial component of SAR applications. Vision-language Models (VLMs) play an important role in SAR target interpretation. By incorporating natural language understanding, VLMs effectively address the challenges posed by large intraclass variability in target characteristics and the scarcity of high-quality labeled samples, thereby advancing the field from purely visual interpretation toward semantic understanding of targets. Drawing upon our team’s extensive research experience in SAR target interpretation theory, algorithms, and applications, this paper provides a comprehensive review of intelligent SAR target interpretation based on VLMs. We provide an in-depth analysis of existing challenges and tasks, summarize the current state of research, and compile available open-source datasets. Furthermore, we systematically outline the evolution, ranging from task-specific VLMs to contrastive-, conversational-, and generative-based VLMs and foundational models. Finally, we discuss the latest challenges and future outlooks in SAR target interpretation by VLMs.

     

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