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An Adaptive Space Target Detection Algorithm
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AI
DOI：10.1109/LGRS.2022.3214661.png)
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Abstract
En 中文
In space target monitoring system, due to the interference of noise, stray light, and the limitations of the image acquisition system, there are nonuniform background and smear phenomena in star images. Moreover, there are also a large number of background stars in star images. All these factors lead to the difficulty of space target detection. In order to improve the detection performance, an adaptive space target detection algorithm is proposed in this letter. First, in order to suppress the complex background interference in the star image, a window adaptive bidirectional 1-D median filtering method is proposed, whose window size is set based on the maximum star size in the first star image. Then, background correction is performed by bidirectional 1-D median filter. Second, in order to improve the reliability of space target segmentation, an improved interframe difference method is proposed, which consists of two parts: 1) the bright line residue is eliminated based on a horizontal 1-D median filter and 2) the target segmentation threshold is adaptively set based on the Kalman filter method. Compared with existing algorithms, our proposed method can detect space targets quickly with low false alarm rate and high detection rate under complex background conditions.
Keywords:
Stars
Signal to noise ratio
Object detection
Geoscience and remote sensing
Filtering
Kalman filters
Geology
Adaptability
Kalman filter
median filter
space debris
target detection
Journal
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16.4
Papers:
1.0W
Citations:
5.1K