An Autonomous SAR Image Interpretation Algorithm Based on Multi-Agent Collaborative Scheduling

Synthetic Aperture Radar (SAR) image interpretation in dynamic scenarios faces critical challenges, including sluggish multi-agent scheduling responses, sub-optimal task-resource matching, and low full-pipeline collaborative efficiency. To address these issues, this paper proposes an autonomous SAR image interpretation algorithm based on a Mission Control Point (MCP)-driven centralized multi-agent collaborative scheduling framework. To address inefficient task–resource matching, a multi-source orchestration model integrating agent states, task characteristics, and environmental dynamics is developed for optimized initial allocation. To mitigate information fragmentation and improve collaboration efficiency across the pipeline, an MCP-based centralized architecture is proposed to achieve unified scheduling and global optimization of multi-stage agents. Furthermore, to enhance adaptability in dynamic environments, a verification-driven adaptive policy continuous optimization mechanism is introduced, allowing the scheduling policy to continuously adapt. Experiments have been conducted on the SARCAP public dataset, and the proposed method achieved a task–agent matching accuracy of 97.98%, an average scheduling latency of 66.1 ms, and a collaborative interpretation speed of 17.9 fps. Compared with MAPPO and conventional centralized scheduling, scheduling efficiency was improved by 12.3% and 18.7%, respectively. Ablation studies further indicate that both the MCP centralized scheduling mechanism and the multi-source information orchestration module significantly contributed to performance, ensuring high accuracy and robustness.