# American Institute of Mathematical Sciences

August & September  2019, 12(4&5): 1233-1249. doi: 10.3934/dcdss.2019085

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

 1 Beijing Research Center for Information Technology, in Agriculture, Beijing 100097, China 2 National Engineering Research Center for Information, Technology in Agriculture, Beijing 100097, China 3 Beijing Key Lab of Digital Plant, Beijing 100097, China 4 Department of Mathematics and Statistics, The University of North Carolina at Greensboro Greensboro, NC, 27402, United States

* Corresponding author: Xinyu Guo

Received  August 2017 Revised  January 2018 Published  November 2018

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.

Citation: Jiangchuan Fan, Xinyu Guo, Jianjun Du, Weiliang Wen, Xianju Lu, Brahmani Louiza. Analysis of the clustering fusion algorithm for multi-band color image. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1233-1249. doi: 10.3934/dcdss.2019085
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show all references

##### References:
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SAR image
TM image
The feature extraction results of the test images by Sobel and Canny operators
Comparison results of clustering fusion results by two different algorithms
Observation parameters of TM image
 Sensor Band wavelength ($\mu$m) TM 1 0.45$\sim$0.52 blue light band 2 0.52$\sim$0.60 green light band 3 0.63$\sim$0.69 Red band, above the visible light band; 4 0.76$\sim$0.90 Near infrared band 5 1.22$\sim$1.75 Middle infrared band 6 10.4$\sim$12.5 Thermal infrared band 7 2.08$\sim$2.35 Far infrared band
 Sensor Band wavelength ($\mu$m) TM 1 0.45$\sim$0.52 blue light band 2 0.52$\sim$0.60 green light band 3 0.63$\sim$0.69 Red band, above the visible light band; 4 0.76$\sim$0.90 Near infrared band 5 1.22$\sim$1.75 Middle infrared band 6 10.4$\sim$12.5 Thermal infrared band 7 2.08$\sim$2.35 Far infrared band
evaluation index value of clustering fusion of SAR image and TM image
 Different algorithms Evaluating indicator Average gradient Information entropy Standard deviation Edge retention Traditional algorithm Band 1 22.5114 7.76 7.0154 52.7669 Band 4 22.4716 7.72 7.0692 48.2687 Band 7 21.0537 6.85 6.9837 45.5618 The proposed algorithm Band 1 32.1645 7.26 6.5945 65.3746 Band 4 33.0692 7.31 5.8861 62.3149 Band 7 32.8974 6.43 5.8233 55.8862
 Different algorithms Evaluating indicator Average gradient Information entropy Standard deviation Edge retention Traditional algorithm Band 1 22.5114 7.76 7.0154 52.7669 Band 4 22.4716 7.72 7.0692 48.2687 Band 7 21.0537 6.85 6.9837 45.5618 The proposed algorithm Band 1 32.1645 7.26 6.5945 65.3746 Band 4 33.0692 7.31 5.8861 62.3149 Band 7 32.8974 6.43 5.8233 55.8862
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