A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation
For researchers and practitioners in SAR data augmentation, SAGA provides a structured, quality-assured approach to handle heterogeneous datasets and generation methods, improving the reliability of augmented data for downstream interpretation tasks.
SAGA is a schema-grounded agent framework for task-oriented SAR data augmentation that improves reliability and reproducibility by separating semantic proposal from deterministic validation. Experiments show it outperforms baselines in schema grounding, skill planning, invalid-sample rejection, and downstream augmentation utility.
Synthetic aperture radar (SAR) data augmentation is important for improving the generalization of data-driven SAR interpretation models, yet practical augmentation workflows are often hindered by heterogeneous dataset formats, task-dependent metadata requirements, diverse generation methods, and weak validation of generated samples. This paper presents the \textbf{S}AR \textbf{A}ugmentation and \textbf{G}eneration \textbf{A}gent (SAGA), a schema-grounded and benefit-aware agent framework for task-oriented SAR data generation and augmentation. Given a natural-language request and heterogeneous SAR inputs, SAGA extracts observable dataset facts, validates executable dataset schemas, selects feasible augmentation strategies through validator-constrained planning, and compiles the selected strategy into an auditable augmentation workflow. Generated data are further assessed by quality, distribution, SAR-artifact, duplicate, leakage, and optional downstream-task evaluators to support evidence-qualified augmentation claims. By separating semantic proposal from deterministic validation and execution, SAGA improves the reliability and reproducibility of SAR augmentation decisions. Experiments on controlled agentic benchmarks and downstream SAR interpretation tasks show that SAGA improves schema grounding, skill planning, invalid-sample rejection, and downstream augmentation utility compared with rule-based, LLM-only, ReAct-style, and fixed-augmentation baselines.