亚像素仿射配准空间碎片图像：基于Radon点扩散函数

中文摘要 AI 辅助

由平台抖动和姿态调整引起的帧间仿射错位，对光学监视中多帧点目标分析构成了根本性挑战。传统配准方法依赖空间强度相关性或显著图像特征，而在低信噪比点目标图像中，这两者均基本缺失。我们引入Radon点扩散函数（RPSF）来表征Radon变换域中的点目标，并推导出一个闭式框架，该框架可仅从每帧对的四个标量RPSF样本联合估计帧间平移和旋转。该方法无需迭代优化、特征提取或插值，适用于资源受限的星上处理。仿真结果证实了亚像素平移精度，并在1° Radon角分辨率下实现了0.2556°的平均旋转误差。在五个真实空间碎片数据集（包括地基和在轨观测）上的验证显示，平均校准误差低于0.5像素，远超可靠多帧处理所需的精度。

英文摘要

Inter-frame affine misalignment caused by platform jitter and attitude adjustments poses a fundamental challenge for multi-frame analysis of point targets in optical surveillance. Conventional registration methods rely on spatial intensity correlations or distinctive image features, both of which are largely absent in low-signal-to-noise-ratio point target imagery. We introduce the Radon Point Spread Function (RPSF) to characterize point targets in the Radon-transformed domain, and derive a closed-form framework that jointly estimates inter-frame translation and rotation from as few as four scalar RPSF samples per frame pair. The method requires no iterative optimization, feature extraction or interpolation, which is suitable for resource-constrained onboard processing. Simulation results confirm sub-pixel translation accuracy and a mean rotation error of 0.2556° at 1° Radon angular resolution. Validation on five real space debris datasets including both ground-based and in-orbit observations yields a mean calibration error below 0.5 pixels, substantially exceeding the precision required for reliable multi-frame processing.

发表机构

• Beihang University（北京航空航天大学）
• China Academy of Space Technology（中国空间技术研究院）

机构由 AI 辅助整理，请以论文原文为准。