Summary
Tracking small pieces of space junk using optical images is tricky because the images can shift and rotate between frames due to the movement of the camera platform. Traditional methods struggle when the images are very noisy and lack clear features. The authors developed a new approach that uses a mathematical tool called the Radon transform to precisely measure how each image moves and turns relative to others. This method can find small shifts and rotations quickly and accurately, even with limited computing power, which is helpful for real-time space debris monitoring. Tests with both simulated and real data showed that their technique is very precise, better than what is needed for combining multiple images effectively.
image registrationspace debrisaffine transformationRadon transformpoint spread functionsub-pixel accuracyoptical surveillanceimage alignmentlow signal-to-noise ratiomulti-frame analysis
Authors
Shenshen Luan, Miaomiao Tian, Shuai Jiang, Yan Yang, Shuguo Xie, Zezhou Sun
Abstract
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.