A background rejection method based on star-point matching in star-background image

摘要

This paper addresses the problem of rejecting fixed star background in star-background image. For most sensors with a fine spatial resolution, phenomenological effects, such as background, and system effects, such as noise, contribute significant numbers of spurious points to each frame. In star-background images, fixed stars are uppermost source of spurious points. Since background and noise do not behave like targets, a good tracking algorithm would eventually reject the spurious points as non-targets. However, the computation required to consider which points appearing in a frame are from the target grows geometrically with the number of points to be considered. Simply considering each of these points as a candidate target point unnecessarily burdens the tracking algorithm and in many cases would require computational resources that cannot be provided to the mission. In this paper, we proposed a new method for rejecting fixed stars based on star-point matching in star-background image. We decide the fixed stars using point matching between points in actual image and points in ideal image which relies on the catalog. This work extends applied domain of Hausdorff Distance (HD) which is one of commonly used measures for object matching. In our experiments, Least Trimmed Square HD (LTS-HD) was used in point matching, and the result is effective.