基于视觉语言模型的SAR图像目标解译综述OA

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.