SAR-RAG: ATR visual question answering by semantic search, retrieval, and MLLM generation

We present SAR-RAG, an agentic image-retrieval-augmented generation (ImageRAG) framework for automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery. SAR is widely used in defense and security, but visually similar vehicle signatures, speckle, and sensor-dependent effects can complicate interpretation. SAR-RAG combines a multimodal large language model (MLLM) with a vector database of semantic SAR embeddings to enable contextual search over a library of previously observed, labeled exemplars with known target types and associated attributes. During inference, the agent retrieves the most relevant reference images and uses them as an attached “memory bank” to support comparison and reasoning across closely related vehicle categories, improving both categorical identification and quantitative estimation. We evaluate the approach using retrieval quality measures, ATR classification accuracy, and regression of vehicle dimensions, and show consistent improvements over an MLLM-only baseline when retrieval-augmented context is incorporated.