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Proceedings Paper

A new method for detecting small and dim targets in starry background
Author(s): Rui Yao; Yanning Zhang; Lei Jiang
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Paper Abstract

Small visible optical space targets detection is one of the key issues in the research of long-range early warning and space debris surveillance. The SNR(Signal to Noise Ratio) of the target is very low because of the self influence of image device. Random noise and background movement also increase the difficulty of target detection. In order to detect small visible optical space targets effectively and rapidly, we bring up a novel detecting method based on statistic theory. Firstly, we get a reasonable statistical model of visible optical space image. Secondly, we extract SIFT(Scale-Invariant Feature Transform) feature of the image frames, and calculate the transform relationship, then use the transform relationship to compensate whole visual field's movement. Thirdly, the influence of star was wiped off by using interframe difference method. We find segmentation threshold to differentiate candidate targets and noise by using OTSU method. Finally, we calculate statistical quantity to judge whether there is the target for every pixel position in the image. Theory analysis shows the relationship of false alarm probability and detection probability at different SNR. The experiment result shows that this method could detect target efficiently, even the target passing through stars.

Paper Details

Date Published: 16 August 2011
PDF: 8 pages
Proc. SPIE 8196, International Symposium on Photoelectronic Detection and Imaging 2011: Space Exploration Technologies and Applications, 81960C (16 August 2011); doi: 10.1117/12.899158
Show Author Affiliations
Rui Yao, Northwestern Polytechnical Univ. (China)
Yanning Zhang, Northwestern Polytechnical Univ. (China)
Lei Jiang, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 8196:
International Symposium on Photoelectronic Detection and Imaging 2011: Space Exploration Technologies and Applications
John C. Zarnecki; Carl A. Nardell; Rong Shu; Jianfeng Yang; Yunhua Zhang, Editor(s)

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