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:
[1]

R. Adillon and L. Jorba, Quantified trapezoidal fuzzy numbers, Journal of Intelligent & Fuzzy Systems, 33 (2017), 601-611.   Google Scholar

[2]

E. BacoO. UkimuraE. RudL. VlatkovicA. SvindlandM. AronS. PalmerT. MatsugasumiA. Marien and J. C. Bernhard, Magnetic resonance imaging-transectal ultrasound image-fusion biopsies accurately characterize the index tumor: correlation with step-sectioned radical prostatectomy specimens in 135 patients, European Urology, 67 (2015), 787-794.   Google Scholar

[3]

I. BarnaureP. PollakS. MomjianJ. HorvathK. O. LovbladC. Bo xJ. RemuinanP. Burkhard and M. I. Vargas, Evaluation of electrode position in deep brain stimulation by image fusion (mri and ct)., Neuroradiology, 57 (2015), 903-908.   Google Scholar

[4]

A. Bicer, Capraro and m.m. capraro, integrated stem assessment model, vol. 13, 2017, 3959-3968. Google Scholar

[5]

M. BonamyB. LévÊque and A. Pinlou, Planar graphs with delta $\ge 7$ and no triangle adjacent to a c-4 are minimally edge and total choosable, Discrete Mathematics and Theoretical Computer Science, 17 (2016), 131-145.   Google Scholar

[6]

P. S. Brezavscek A. and A. Znidarsic, Factors influencing the behavioural intention to use statistical software: The perspective of the slovenian students of social sciences, Eurasia Journal of Mathematics Science and Technology Education, (), 953-986.   Google Scholar

[7]

C. Chen and J. Shi, Chinese local government's behavior in land supply in the context of housing market macro-control, Journal of Interdisciplinary Mathematics, 20 (2017), 1289-1306.   Google Scholar

[8]

G. H. ChenY. Zhao and B. Su, Raw material inventory optimization for mto enterprises under price fluctuations, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 255-270.   Google Scholar

[9]

M. FeiW. JiangW. Mao and Z. Song, New fusional framework combining sparse selection and clustering for key frame extraction, Iet Computer Vision, 10 (2016), 280-287.   Google Scholar

[10]

W. Gao and W. Wang, New isolated toughness condition for fractional (g, f, n)-critical graphs, Colloquium Mathematicum, 147 (2017), 55-65.  doi: 10.4064/cm6713-8-2016.  Google Scholar

[11]

M. Ghahremani and H. Ghassemian, Remote sensing image fusion using ripplet transform and compressed sensing, International Journal of Remote Sensing, 12 (2015), 4131-4143.   Google Scholar

[12]

D. Guo, J. Yan and X. Qu, High quality multi-focus image fusion using self-similarity and depth information, Optics Communications, 138-144. Google Scholar

[13]

F. Jing, M. Li, H. J. Zhang and B. Zhang, Spectral clustering image segmentation based on sparse matrix, 4 (2017), 1308-1313. Google Scholar

[14]

Y. LiC. JiaX. KongL. Yang and J. Yu, Locally weighted fusion of structural and attribute information in graph clustering, IEEE Transactions on Cybernetics, PP (2017), 1-14.   Google Scholar

[15]

J. LiangY. Han and Q. Hu, Semi-supervised image clustering with multi-modal information, Multimedia Systems, 22 (2016), 149-160.   Google Scholar

[16]

H. J. Liu and X. B. Liu, Relationship between paternalistic leadership and employee's voice behavior based on regression analysis, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 205-215.   Google Scholar

[17]

P. S., B. M. and R. G., Tephra redeposition and mixing in a late-glacial hillside basin determined by fusion of clustering analyses of glass-shard geochemistry, in Journal of Quaternary Science, 82 (2000), 789-802. Google Scholar

[18]

A. Takahashi, Development status of condensed cluster fusion theory, Current Science, 108 (2015), 514-515.   Google Scholar

[19]

B. K. Teh and S. A. Cheong, Cluster fusion-fission dynamics in the singapore stock exchange, European Physical Journal B, 88 (2015), 1-14.   Google Scholar

[20]

C. Y. WangW. ZhaoQ. Liu and H. W. Chen, Optimization of the tool selection based on big data, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 341-360.   Google Scholar

[21]

G. WeiS. Li and M. R. Farahani, Szeged related indices of tuac6[p, q], Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 553-563.  doi: 10.1080/09720529.2016.1228312.  Google Scholar

[22]

Z. Y. Q. and T. J. L., Clustering sorting algorithm based on digital channelized receiver, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 143-148. Google Scholar

[23]

F. X. Yang, I/o mapping algorithm based on semantic overlay fusion, Bulletin of Science and Technology, 60-62. Google Scholar

[24]

S. YinL. CaoY. Ling and G. Jin, One color contrast enhanced infrared and visible image fusion method, Infrared & Laser Engineering, 53 (2010), 146-150.   Google Scholar

[25]

J. Zhang and H. H. Fan, An improved image segmentation algorithm and simulation based on fuzzy clustering, Computer Simulation. Google Scholar

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 $2.35 Far infrared band
SensorBandwavelength ($\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
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
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
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