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Spectroscopy and Spectral Analysis, Volume. 42, Issue 4, 1285(2022)

Adaptability Analysis of Multiple Features Detection Algorithms Based on Fusion Degree Model Between Target and Environment

Xian-ming DENG1,1;... Tian-cai ZHANG1,*, Zeng-can LIU1,1;, Zhong-sheng LI1,1;, Jie XIONG1,1;, Yi-xiang ZHANG1,1;, Peng-hao LIU1,1;, Yi CEN2,2; *; and Fa-lin WU1,1; |Show fewer author(s)
Author Affiliations
  • 11. The 59th Research Institute of China Ordnance Industry, Chongqing 400039, China
  • 22. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
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    References (6)
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    Figures & Tables(14)
    Test scenarios(a): Grassland background and vegetation camouflage target; (b): Soil background and vegetation camouflage target; (c): Soil background and vegetation and cement road camouflage targets; (d): Grassland/cement road/soil background and its corresponding camouflage targets

    Fig. 1. Test scenarios
    (a): Grassland background and vegetation camouflage target; (b): Soil background and vegetation camouflage target; (c): Soil background and vegetation and cement road camouflage targets; (d): Grassland/cement road/soil background and its corresponding camouflage targets

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    Detection results of vegetation camouflage targets under grassland background using different algorithms(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

    Fig. 2. Detection results of vegetation camouflage targets under grassland background using different algorithms
    (a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

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    Detection results of vegetation camouflage targets under soil background using different algorithms(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

    Fig. 3. Detection results of vegetation camouflage targets under soil background using different algorithms
    (a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

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    Detection results of vegetation and soil camouflage targets under soil background using different algorithms(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

    Fig. 4. Detection results of vegetation and soil camouflage targets under soil background using different algorithms
    (a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

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    Detection results of vegetation/cement road/soil camouflage targets under grassland/cement road/soil background using different algorithms(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

    Fig. 5. Detection results of vegetation/cement road/soil camouflage targets under grassland/cement road/soil background using different algorithms
    (a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

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    Detection results of four scenarios by nine multiple features detection algorithms

    Fig. 6. Detection results of four scenarios by nine multiple features detection algorithms

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    Detection results of three SNR scenarios by nine multiple features detection algorithms

    Fig. 7. Detection results of three SNR scenarios by nine multiple features detection algorithms

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    • Table 1. Test scheme

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      Table 1. Test scheme

      NumberTest SchemesSimilarity between
      targets      		Similarity between target
      and background
      1Detection of vegetation camouflage targets under grassland backgroundlargelarge
      2Detection of vegetation camouflage targets under soil backgroundlargesmall
      3Detection of vegetation and road camouflage targets under soil backgroundsmallsmall
      4Detection of vegetation, soil, road camouflage targets under grassland, soil, road backgroundsmalllarge

    • Table 2. FD and accuracy parameter of test 1

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      Table 2. FD and accuracy parameter of test 1

      NumberAlgorithmsFDAccuracy
      1MtACE0.315 31.0
      2MtAMF0.394 90.921 8
      3MtCEM0.736 10.865 5
      4SumACE0.391 50.931 1
      5SumAMF0.391 50.931 1
      6SumCEM0.858 80.864 2
      7WtaACE0.666 80.683 6
      8WtaAMF0.666 80.683 6
      9WtaCEM0.937 40.642 1

    • Table 3. FD and accuracy parameter of test 2

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      Table 3. FD and accuracy parameter of test 2

      NumberAlgorithmsFDAccuracy
      1MtACE0.318 61.0
      2MtAMF0.372 30.944 2
      3MtCEM0.878 80.689 0
      4SumACE0.366 60.956 4
      5SumAMF0.366 60.956 4
      6SumCEM0.861 10.864 0
      7WtaACE0.744 50.753 5
      8WtaAMF0.744 50.753 5
      9WtaCEM0.964 30.593 5

    • Table 4. FD and accuracy parameter of test 3

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      Table 4. FD and accuracy parameter of test 3

      NumberAlgorithmsFDAccuracy
      1MtACE0.353 61.0
      2MtAMF0.382 10.905 8
      3MtCEM0.878 11.726 9
      4SumACE0.430 20.940 2
      5SumAMF0.430 20.940 2
      6SumCEM0.914 70.805 5
      7WtaACE0.691 60.712 5
      8WtaAMF0.691 60.712 5
      9WtaCEM0.986 80.457 3

    • Table 5. FD and accuracy parameter of test 4

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      Table 5. FD and accuracy parameter of test 4

      NumberAlgorithmsFDAccuracy
      1MtACE0.292 41.0
      2MtAMF0.251 60.920 4
      3MtCEM0.956 40.640 1
      4SumACE0.275 00.956 2
      5SumAMF0.275 00.956 2
      6SumCEM0.749 80.936 3
      7WtaACE0.616 90.632 5
      8WtaAMF0.616 90.632 5
      9WtaCEM0.904 30.796 8

    • Table 6. Detection results under different signal-to-noise ratios

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      Table 6. Detection results under different signal-to-noise ratios

      NumberAlgorithmsSNR=200SNR=200SNR=400SNR=400SNR=800SNR=800
      FDAccuracyFDAccuracyFDAccuracy
      1MtACE0.381 20.754 30.305 10.821 10.254 30.873 8
      2MtAMF0.394 50.860 10.306 40.886 60.261 70.922 2
      3MtCEM0.773 30.821 50.748 20.839 00.802 70.874 3
      4SumACE0.449 60.842 40.361 20.873 00.297 90.917 9
      5SumAMF0.449 60.842 40.361 20.873 00.297 90.917 9
      6SumCEM0.841 80.791 00.843 60.813 30.857 20.857 7
      7WtaACE0.691 50.701 10.695 90.705 80.699 50.699 8
      8WtaAMF0.691 50.701 10.695 90.705 80.699 50.699 8
      9WtaCEM0.956 60.574 10.962 90.552 70.961 40.562 4

    • Table 7. Statistics of detection mean and standard deviation of multi-feature detection algorithms in different scenarios

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      Table 7. Statistics of detection mean and standard deviation of multi-feature detection algorithms in different scenarios

      AlgorithmsTests of spectrumTests of noise
      MeanVarianceMeanVariance
      MtACE0.320 00.021 90.313 50.052 1
      MtAMF0.350 20.057 50.320 90.055 2
      MtCEM0.862 40.079 50.774 70.022 3
      SumACE0.365 80.057 10.369 60.062 2
      SumAMF0.365 80.057 10.369 60.062 2
      SumCEM0.846 10.059 90.847 50.006 9
      WtaACE0.680 00.046 00.695 60.003 3
      WtaAMF0.680 00.046 00.695 60.003 3
      WtaCEM0.948 20.030 80.960 30.002 7
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    Xian-ming DENG, Tian-cai ZHANG, Zeng-can LIU, Zhong-sheng LI, Jie XIONG, Yi-xiang ZHANG, Peng-hao LIU, Yi CEN, Fa-lin WU. Adaptability Analysis of Multiple Features Detection Algorithms Based on Fusion Degree Model Between Target and Environment[J]. Spectroscopy and Spectral Analysis, 2022, 42(4): 1285

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    Paper Information

    Category: Research Articles

    Received: Dec. 11, 2020

    Accepted: --

    Published Online: Jul. 25, 2023

    The Author Email: ZHANG Tian-cai (cenyi@radi.ac.cn)

    DOI:10.3964/j.issn.1000-0593(2022)04-1285-08

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology