Multiple dim targets detection in infrared image sequences

Tian-Lei Ma; Ze-lin Shi; Jian Yin; Bao-shu Xu

doi:10.1117/12.2072844

24 November 2014 Multiple dim targets detection in infrared image sequences

Tian-Lei Ma, Ze-lin Shi, Jian Yin, Bao-shu Xu

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93012K (2014) https://doi.org/10.1117/12.2072844
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

Abstract

Strong noises interference is a difficult technical problem for signals detection. Multiple targets detection with strong noises makes the problem more complicated. Aiming at the difficulty of multiple uniform rectilinear motion targets detection in infrared (IR) image sequences with strong noises, this paper presents a multiple dim targets detection algorithm which improves signal-to-noise ratio (SNR). Firstly, we establish a velocity space and stack image sequences along different velocity vectors. Secondly, mean filtering in time-domain is applied to stacked images. Thirdly, quasi-target points in mean filtering images are selected by constant false-alarm ratio (CFAR) judging. Finally, coordinate vectors and velocity vectors of quasi-target points are mapped to location space and velocity space, respectively. As a result, local peaks from the two spaces will confirm target points; meanwhile, velocity vectors of targets can also be acquired. In addition, effect of velocity steps on SNR improvement is analyzed, which can guide the selection of steps and reduce computational burden. Both moving dim targets simulation experiment and real-world dim targets detection experiment have proved that this algorithm can effectively detect multiple dim targets under strong noise background.

Citation Download Citation

Tian-Lei Ma, Ze-lin Shi, Jian Yin, and Bao-shu Xu "Multiple dim targets detection in infrared image sequences", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93012K (24 November 2014); https://doi.org/10.1117/12.2072844

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