Constant false alarm rate algorithm for the dim-small target detection based on the distribution characteristics of target coordinates

Xiao-Liang Fei; Kan Ren; Wei-xian Qian; Peng-cheng Wang

doi:10.1117/12.2197891

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8 October 2015 Constant false alarm rate algorithm for the dim-small target detection based on the distribution characteristics of target coordinates

Xiao-Liang Fei; Kan Ren; Wei-xian Qian; Peng-cheng Wang

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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96750R (2015) https://doi.org/10.1117/12.2197891
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

CFAR (Constant False Alarm Rate) is a key technology in Infrared dim-small target detection system. Because the traditional constant false alarm rate detection algorithm gets the probability density distribution which is based on the pixel information of each area in the whole image and calculates the target segmentation threshold of each area by formula of Constant false alarm rate, the problems including the difficulty of probability distribution statistics and large amount of algorithm calculation and long delay time are existing. In order to solve the above problems effectively, a formula of Constant false alarm rate based on target coordinates distribution is presented. Firstly, this paper proposes a new formula of Constant false alarm rate by improving the traditional formula of Constant false alarm rate based on the single grayscale distribution which objective statistical distribution features are introduced. So the control of false alarm according to the target distribution information is implemented more accurately and the problem of high false alarm that is caused of the complex background in local area as the cloud reflection and the ground clutter interference is solved. At the same time, in order to reduce the amount of algorithm calculation and improve the real-time characteristics of algorithm, this paper divides the constant false-alarm statistical area through two-dimensional probability density distribution of target number adaptively which is different from the general identifying methods of constant false-alarm statistical area. Finally, the target segmentation threshold of next frame is calculated by iteration based on the function of target distribution probability density in image sequence which can achieve the purpose of controlling the false alarm until the false alarm is down to the upper limit. The experiment results show that the proposed method can significantly improve the operation time and meet the real-time requirements on condition of keeping the target detection performance.

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Xiao-Liang Fei, Kan Ren, Wei-xian Qian, and Peng-cheng Wang "Constant false alarm rate algorithm for the dim-small target detection based on the distribution characteristics of target coordinates", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750R (8 October 2015); https://doi.org/10.1117/12.2197891