A Probabilistic Approach of Space Objects Detection from Non-resolved Optical Observation

摘要

Non-resolved optical space imagery is usually heavily noisy with many field stars clutter. Both phenomena may impair the success of detecting space objects. There is hence often a need for efficient and robust preprocessing techniques to filter the objects of interest. In this paper, we propose a novel probabilistic approach for denoising, filtering and detecting space objects by using non-resolved optical images. In particular, as pixels of background and foreground in an image obey different probabilistic distributions, we propose a corresponding clustering algorithm to distinguish foreground objects from background noises. Furthermore, a near real-time finer classification for foreground objects is achieved by further exploring the metric on the filtered pixel set. Various modes (sidereal tracking/stationary) and different types of objects (GEO/LEO) are unified into this general framework. We verify the effectiveness and robustness of our algorithm by detecting and filtering space objects in CCD telescopic sequential imageries under different experimental conditions.