Abstract
Long exposure time and wide field can effectively improve the ability of a space surveillance telescope to detect faint space targets. However, complicated situations pose challenges for space target detection. Background star images usually manifest a rotated streak, and target trajectories can be crossed, discontinuous, or nonlinear. This paper presents an accurate and robust space target detection method, namely, spatiotemporal pipeline multistage hypothesis testing (SPMHT), to overcome the issues. Specifically, the method includes the following two stages: First, in the spatiotemporal pipeline filtering step, Spatiotemporal-related Intersection over Union (SrIoU) is used to calculate the IoU score instead of the traditional method. Benefiting from the differences between motion characteristics of targets and stars and the insensitivity of the SrIoU score to the noise, the spatiotemporal pipeline filtering can effectively remove the streak images of background stars and obtain candidate points. Second, a series of candidate points is further organized into a tree structure. We pruned in the tree structure combined with these candidate trajectories by using velocity and direction feature of moving objects. Furthermore, in the search step, fast adaptive sequence region search is used to reduce the computational cost. The experimental results for two datasets, simulated image datasets and real captured image datasets, demonstrate the effectiveness in addressing the difficulties of space target detection in complicated situations.
Highlights
Space targets, including nonfunctional artificial objects, spent upper stages, and apogee boost motors, are mainly satellites and space debris of all sizes in near-earth space
In the case of long exposure time and wide field of view, background stars are affected by platform vibration and tracking error and usually manifest a streak-like form, which are similar to the shape of space targets
Compared with the traditional classification method based on gray value correlation, Spatiotemporal-related Intersection over Union (SrIoU) score has the better detection performance for low signalto-noise ratio (SNR) targets
Summary
Space targets, including nonfunctional artificial objects, spent upper stages, and apogee boost motors, are mainly satellites and space debris of all sizes in near-earth space. These methods are effective in detecting faint and small space targets in image sequences These algorithms are limited by the large computational cost that comes with the large number of possible trajectories that must be searched. Many improved algorithms, such as SB–MHT [29], MMHTT [30], and TMQHT [31], have been proposed subsequently These methods are effective in detecting and tracking faint small objects in the star background, the following difficulties and challenges still exist. Complicated situations will pose difficulties and challenges for space target detection To solve these problems, we propose a high-accuracy and robust two-stage object detection framework named Spatiotemporal Pipeline Multistage Hypothesis Testing (SPMHT) while maintaining a low computational cost.
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