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Analysis of the clustering fusion algorithm for multi-band color image

  • * Corresponding author: Xinyu Guo

    * Corresponding author: Xinyu Guo 
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  • Because of the limitations of the technical conditions, the traditional algorithms can not be mapped with many kinds of color, and the treatment effect is poor, which is not conducive to human eye observation. A clustering fusion algorithm based on D-S evidence theory is proposed in this paper to make salt denoising and Gauss denoising operation for the multi-band color image, to improve the image recognition, and better reflect the objective reality, which is not limited by technical conditions; the denoised images are made texture features and edge features extraction; these two kinds of features are fused and carried out the probability distribution to solve the probability of that the each pixel belongs to each class; Based on the DS evidence combination, the probability of four channels is fused, and according to the probability of what kind of each pixel belonging is the largest, it is clustered. Experimental results show that the proposed algorithm can combine different bands of color images to different levels of target features, and retain more effective information, which is conducive to target recognition and detection.

    Mathematics Subject Classification: 78A50.

    Citation:

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  • Figure 1.  SAR image

    Figure 2.  TM image

    Figure 3.  The feature extraction results of the test images by Sobel and Canny operators

    Figure 4.  Comparison results of clustering fusion results by two different algorithms

    Table 1.  Observation parameters of TM image

    SensorBandwavelength ($\mu $m)
    TM10.45$\sim $0.52 blue light band
    20.52$\sim $0.60 green light band
    30.63$\sim $0.69 Red band, above the visible light band;
    40.76$\sim $0.90 Near infrared band
    51.22$\sim $1.75 Middle infrared band
    610.4$\sim $12.5 Thermal infrared band
    72.08$\sim $2.35 Far infrared band
     | Show Table
    DownLoad: CSV

    Table 2.  evaluation index value of clustering fusion of SAR image and TM image

    Different algorithms Evaluating indicatorAverage gradientInformation entropyStandard deviationEdge retention
    Traditional algorithmBand 122.51147.767.015452.7669
    Band 422.47167.727.069248.2687
    Band 721.05376.856.983745.5618
    The proposed algorithmBand 132.16457.266.594565.3746
    Band 433.06927.315.886162.3149
    Band 732.89746.435.823355.8862
     | Show Table
    DownLoad: CSV
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