Citation: | Luo Zhenjie, Zeng Guoqiang. Space objects detection in video satellite images using improved MTI algorithm[J]. Opto-Electronic Engineering, 2018, 45(8): 180048. doi: 10.12086/oee.2018.180048 |
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Overview: Space exploration activities are becoming more frequent, resulting in an increased population of space debris and even an increased risk of on-orbit collisions. In order to prevent on-orbit collision accidents, many countries have conducted space objects detection projects. Space-based observation is an important and effective means for the perception of space objects, attributing to its advantages of being closer to space objects and not constrained by weather and location. With the development of microsatellite technology in recent years, various countries have carried out space-based observations based on microsatellites, such as the STARE Project of US, the three microsatellites of MOST, Sapphire and NEOSSat in Canada. In order to successfully locate the space objects, the realization of dim targets detection based on the sky background image is one of the key technologies. The American SBV plan proposed a classic space objects detection process, which consists of a classical MTI algorithm and a speed filter. However, this algorithm has a long running time. Some scholars also use MTI algorithm and connected domain detection to detect space objects, but they do not consider the situation where the trajectory is discontinuous when the object's speed is a little bit large. To solve the above problems, an improved MTI algorithm is proposed in this paper. We aim to realize the space objects detection in a video satellite's images. In order to detect the inconsecutive target's trajectory, at the beginning of the algorithm we set a special preprocessing which is called pixel's feeling domain. To reduce the time of the algorithm, we simplified the time projection part of the classic MTI algorithm, which is used to restrain the background. After the improved MTI algorithm is processed, targets' trajectories are then obtained through feature-based connected domain detection. When we get the target's trajectory, we can locate the target's position in the image by the centroid method. In summary, the contribution of this paper, namely the improvement of the MTI algorithm, mainly has two points: 1) introduced pixel's feeling domain preprocessing, 2) simplified the time projection part. The experiments in a video satellite's images show that, the improved MTI algorithm can effectively eliminate the background and is suitable for the inconsecutive target's trajectory detection. In addition, the algorithm's processing speed almost meets the real-time task.
The classical detection algorithm framework of SBV
The algorithm framework in this paper
Target's position in adjacent frames
Target's position in adjacent frames and the middle pixel's feeling domain
A frame of video image capture. (a) Video 1; (b) Video 2
The detection results of the algorithm in this paper. (a) The detection result of video 1; (b) The detection result of video 2
Contrast before and after setting pixel's feeling domain. (a) Partial map of the object trajectory while using pixel's feeling domain setting in video 1; (b) Partial map of the object trajectory while using pixel's feeling domain setting in video 2; (c) Partial map of the object trajectory while no pixel's feeling domain setting in video 1; (d) Partial map of the object trajectory while using pixel's feeling domain setting in video 2