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Volume 44 Issue 5
May  2022
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FAN Xiangsuo, FAN Jinlong, WEN Lianghua, XU Zhiyong. Infrared Dim-Small Target Detection Based on Improved Spatio-Temporal Filtering[J]. Infrared Technology , 2022, 44(5): 475-482.
Citation: FAN Xiangsuo, FAN Jinlong, WEN Lianghua, XU Zhiyong. Infrared Dim-Small Target Detection Based on Improved Spatio-Temporal Filtering[J]. Infrared Technology , 2022, 44(5): 475-482.
shu

Infrared Dim-Small Target Detection Based on Improved Spatio-Temporal Filtering

  • 1.

    Division of Intelligent Manufacturing, Yibin University, Yibin 644600, China

  • 2.

    Guangxi Earthmoving Machinery Collaborative Innovation Center, Guangxi University of Science and Technology, Liuzhou 545006, China

  • 3.

    National Satellite Meteorological Center, Beijing 100081, China

  • 4.

    Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China

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  • Received Date: January 10, 2021
  • Revised Date: June 27, 2021
    Graphical Abstract
    • Abstract
  • To effectively solve the problem of low detection rates of dim and small targets caused by dynamic background changes, a detection method based on spatio-temporal filtering is proposed in this paper. Based on an analysis of the imaging characteristics of infrared images, an improved anisotropic spatial filtering algorithm is proposed to evaluate the difference in various gradient characteristics of the target area, background area, and edge contour area. The algorithm fully utilizes the gradient information in the spatial domain to construct the diffusion filter function in different directions. According to the gradient difference in various characteristics of the image, the mean value of the two directions with the smallest value of the diffusion function is selected as the result of spatial filtering to retain the target signal to the maximum extent. To effectively enhance the energy of dim and small targets and address the shortcomings of high-order cumulants that only use the temporal domain information of pixel points for energy enhancement, an energy enhancement algorithm based on spatial-temporal neighborhood blocks is proposed. Experimental results reveal that the proposed algorithm can effectively enhance the detection of dim and small targets in dynamically changing scenes.
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    • References (13)
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