Oriented Clustering Reppoints for Resident Space Objects Detection in Time Delay Integration Images

The rotation of camera during scanning imaging induces the image degradation, which means both resident space objects (RSOs) and stars have the same morphological characteristics in the image. It is difficult to detect RSOs from the stellar background in the scanning imaging mode. In this article, we propose an effective motion perception (EMP) approach to detect RSOs by constructing and perceiving morphological differences between moving RSOs and stationary stars in the scanning imaging mode. Specifically, EMP uses the time-delay integration (TDI) technology to compensate for the image degradation caused by the camera’s rotation, so that different morphologies in the image appear between RSOs and stationary stars. A theoretical method of image motion velocity based on the rigorous imaging model and attitude dynamics is proposed to design TDI imaging parameters, which can accurately compensate for the image degradation. Then, dynamic convolutional-oriented bounding box (DC-OBB) based on the adaptive points representation is used to capture the morphological differences between arbitrarily oriented RSOs and stars. Moreover, a linear perception mechanism based on the streak-like characteristics of RSOs is proposed to choose the representative points during training. It makes the detection head precisely classify and locate targets by increasing the sensitivity of deformable aspect ratio. Experimental results show that our method achieves competitive performance on the high-precision-oriented object detection task.