A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation
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...
| Authors | Xuanting Wu, Fan Zhanga, Fei Ma et al. |
|---|---|
| Primary category | eess.IV |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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