Efficient Attributed Scattering Center Extraction Using Gradient-Based Optimization for SAR Image

The attributed scattering center (ASC) model provides a concise representation of high-frequency electromagnetic scattering of targets, making it widely applicable to synthetic aperture radar target characteristic analysis and recognition. However, ASC extraction, as a non-convex parameter estimation problem, is computationally time-consuming in global optimization due to the non-convexity of loss function. While gradient-based optimization methods in deep learning frameworks can significantly improve efficiency, applying them directly to non-convex optimization remains challenging. To solve this issue, this letter proposes an efficient gradient-based ASC extraction method. First, A more convex-like loss function is designed using windowing techniques to broaden the convergence region and reduce the difficulty of non-convex optimization. Then, an image-frequency domain joint parameter initialization and estimation strategy are developed, achieving fully gradient-based parameter estimation. Validation on simulated and measured data confirms the effectiveness of the proposed method. While maintaining accuracy, it improves ASC extraction efficiency for tank targets from a minute-level to a second-level.