SAIG: Semantic-Aware ISAR Generation via Component-Level Semantic Segmentation

Yuxin Zhao, Huaizhang Liao, Derong Kong, Zhixiong Yang, Jingyuan Xia

Abstract: This letter addresses the challenge of generating high-fidelity inverse synthetic aperture radar (ISAR) images from optical images, particularly for space targets. We propose a framework for the generation of ISAR images incorporating component refinement, which attains high-fidelity ISAR scattering characteristics through the integration of an advanced generation model predicated on semantic segmentation, designated as semantic-aware ISAR generation (SAIG). SAIG renders ISAR images from optical equivalents by learning mutual semantic segmentation maps. Extensive simulations demonstrate its effectiveness and robustness, outperforming state-of-the-art (SOTA) methods by over 8% across key evaluation metrics.

External IDs:doi:10.1109/lgrs.2025.3563712