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Zhang Zhongyu, Jiao Shuhong. Infrared ship target detection method based on multiple feature fusion[J]. Infrared and Laser Engineering, 2015, 44(S1): 29-34.
Citation: Zhang Zhongyu, Jiao Shuhong. Infrared ship target detection method based on multiple feature fusion[J]. Infrared and Laser Engineering, 2015, 44(S1): 29-34.
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Infrared ship target detection method based on multiple feature fusion

  • 1.

    Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China

  • Received Date: 2015-10-05
  • Rev Recd Date: 2015-11-10
  • Publish Date: 2016-01-25
  • Under the ocean background, a segmentation method was proposed based on the fusion of multiple features for infrared ship segmentation. This method is used to extract horizontal edge information and vertical edge information from infrared image. First of all, the mean of the data collected on different scales was got, and the result image was seen as the first feature. Furthermore, for the problem of ship targets with different sizes, the improved multistage filters was employed for infrared image, so as to prohibit background and highlight target. The multistage filtered image was identified as the second feature. Finally, the local maximum gray value of infrared image was identified the third feature. Then these three features would be normalized and integrated. During the process of infusion, firstly, each featue image was given weight; then, an appropriate infusion coefficient for fused image was selected. The fused image was segmented by adaptive threshold, followed by a morphological plastic in order to remove isolated areas, supplement holes and improve the segmentation results. The simulation results show that, compared with traditional segmentation strategies, the proposed segmentation method based on multiple features fusion is more likely to meet the demands of segmentation.
  • [1]
    [2] Ni Lin. Research on algorithms for image segmentation Based on otsu theory[D]. Chongqing: College of Mathematics and Statistics of Chongqing University, 2013. (in Chinese)
    [3]
    [4] Liu Yunhe. Research on IR small target detection and tracking based on attention mechanism[D]. Harbin: Harbin Engineering University, 2009. (in Chinese)
    [5]
    [6]
    [7] Cheng Shenglian. IR small moving target real-time tracking based on multi-level filter[D]. Wuhan: Huazhong University of Science Technology, 2006. (in Chinese)
    [8] Liu Shijun. Under the background of air and infrared ship target recognition methods[D]. Chengdu: University of Electronic Science and Technology of China, 2012.
    [9] Liu Zhaoying, Zhou Fugen, Chen Xiaowu, et al. SunIterative infrared ship target segmentation based on multiple features[J]. Pattern Recognition, 2014, 47(9): 5-10.
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Infrared ship target detection method based on multiple feature fusion

  • 1. Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China
  • Received Date: 2015-10-05
  • Rev Recd Date: 2015-11-10
  • Publish Date: 2016-01-25

Abstract: Under the ocean background, a segmentation method was proposed based on the fusion of multiple features for infrared ship segmentation. This method is used to extract horizontal edge information and vertical edge information from infrared image. First of all, the mean of the data collected on different scales was got, and the result image was seen as the first feature. Furthermore, for the problem of ship targets with different sizes, the improved multistage filters was employed for infrared image, so as to prohibit background and highlight target. The multistage filtered image was identified as the second feature. Finally, the local maximum gray value of infrared image was identified the third feature. Then these three features would be normalized and integrated. During the process of infusion, firstly, each featue image was given weight; then, an appropriate infusion coefficient for fused image was selected. The fused image was segmented by adaptive threshold, followed by a morphological plastic in order to remove isolated areas, supplement holes and improve the segmentation results. The simulation results show that, compared with traditional segmentation strategies, the proposed segmentation method based on multiple features fusion is more likely to meet the demands of segmentation.

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