Name
Abstract | ||
|---|---|---|
Long exposure time and widefield 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 pipelinefiltering 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 pipelinefiltering 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2938454 | IEEE ACCESS |
Keywords | DocType | Volume |
Space target detection,wide-field surveillance,complicated situations,spatiotemporal pipeline,multistage hypothesis testing (MHT) | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengyang Li | 1 | 0 | 0.68 |
| Changxiang Yan | 2 | 0 | 0.68 |
| Chunhui Hu | 3 | 0 | 0.68 |
| Chongyang Liu | 4 | 0 | 0.68 |
| Lizhi Xu | 5 | 0 | 0.34 |