CA-DDPM: Conditionally Embedded Attention-Aided Denoising Diffusion Probabilistic Model for High-Quality SAR Image Generation.
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| Title: | CA-DDPM: Conditionally Embedded Attention-Aided Denoising Diffusion Probabilistic Model for High-Quality SAR Image Generation. |
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| Authors: | Zheng, Yang1 (AUTHOR), Liu, Duhao1,2 (AUTHOR), Li, Ruimin2,3 (AUTHOR), Wang, Rongxu1 (AUTHOR), Fan, Junling2,3 (AUTHOR), Guo, Kaitai1,3 (AUTHOR), Liang, Jimin1 (AUTHOR) jimleung@mail.xidian.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1994. 24p. |
| Subjects: | Synthetic aperture radar, Probabilistic generative models, Generative adversarial networks, Automatic target recognition, Data augmentation, Image quality in imaging systems, Deep learning |
| Abstract: | Highlights: What are the main findings? A novel conditionally embedded attention-aided denoising diffusion probabilistic model (CA-DDPM) is proposed for high-quality, multi-category SAR image generation. A comprehensive evaluation methodology is established to assess SAR image quality through the dimensions of authenticity, diversity, and utility in ATR tasks. What is the implication of the main finding? The established three-dimensional criteria provide a robust benchmark for quantitatively assessing the physical and semantic fidelity of synthetic SAR imagery. CA-DDPM produces more realistic and diverse SAR images than representative GANs and diffusion models, effectively enhancing downstream ATR performance. Deep learning-based automatic target recognition (ATR) for synthetic aperture radar (SAR) imagery requires large quantities of high-quality annotated data, yet real SAR samples are costly and difficult to obtain. Existing generative adversarial network (GAN)-based SAR generation methods often suffer from limited authenticity and insufficient diversity. To address these issues, we propose CA-DDPM, a conditionally embedded attention-aided denoising diffusion probabilistic model (DDPM) for high-quality multi-category SAR image generation. CA-DDPM employs a unified conditional embedding that fuses time-step and category information, injected into a U-Net backbone through a feature-wise linear modulation (FiLM)-based mechanism to achieve step-aware and class-aware denoising. Attention blocks are further incorporated to enhance the modeling of structural dependencies and fine scattering details. To evaluate generation quality, we develop a three-dimensional assessment framework that jointly examines authenticity, diversity, and utility in ATR. Authenticity is quantified using local and global similarity metrics under a unified Hungarian-matched statistical procedure, together with an SAR-adapted Fréchet inception distance (SAR-FID). Diversity is assessed through inter-category feature clustering, an SAR Inception Score (SAR-IS), and a newly proposed intra-category grayscale histogram-based metric. Utility is evaluated by hybrid training experiments across multiple ATR models. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate that CA-DDPM produces more realistic and diverse SAR images than representative GAN- and DDPM-based baselines, and it effectively improves downstream ATR performance through data augmentation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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