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Classification of Satellite Images Using Rp Fuzzy C Means

  • Luis MantillaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1096)

Abstract

The computational capacities increase, the decrease of equipment costs, the growing need for information, among other reasons; It makes possible the increasingly common access to satellite data. In this context. The investigation of techniques related to remote sensing becomes very important because it provide important information about the earth’s surface. Currently, segmentation is an essential step in applications that make use of satellite images. However, the main problem is: “the data in a multispectral image shows a low statistical separation and a long quantity of data”. In this article we propose to improve the balancing of elements for the clusters. We use a new term to estimate the influence that each element must have for the each cluster. This new term can be understood as a repulsion factor and aims to increase the differences between groups. This modification is inspired by new term that was integrated into the NFCC algorithm (New Fuzzy Centroid Cluster).

For the tests, we use the internal validity of the cluster to compare the algorithms. Using the index we measure the characteristics of the segmentation and corroborate them with the final visual results. Therefore, we conclude that the addition of this new term allows balancing the elements for each group. As a result we conclude that the new term organizes the elements better because it avoids a fast convergence of the algorithm. Finally, the results show that this new factor generates clusters with lower entropy and greater similarity between the elements.

Keywords

Segmentation Fuzzy clustering Unsupervised classification Multispectral images 

References

  1. 1.
    Agarwal, P., Kumar, S., Singh, R., Agarwal, P., Bhattacharya, M.: A combination of bias-field corrected fuzzy c-means and level set approach for brain MRI image segmentation. In: 2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI), pp. 84–87. IEEE (2015)Google Scholar
  2. 2.
    Askari, S., Montazerin, N., Zarandi, M.F.: Generalized possibilistic fuzzy c-means with novel cluster validity indices for clustering noisy data. Appl. Soft Comput. 53, 262–283 (2017)CrossRefGoogle Scholar
  3. 3.
    Bai, L., Liang, J., Guo, Y.: An ensemble clusterer of multiple fuzzy \( k \)-means clusterings to recognize arbitrarily shaped clusters. IEEE Trans. Fuzzy Syst. 26(6), 3524–3533 (2018)Google Scholar
  4. 4.
    Bai, X., Wang, Y., Liu, H., Guo, S.: Symmetry information based fuzzy clustering for infrared pedestrian segmentation. IEEE Trans. Fuzzy Syst. 26(4), 1946–1959 (2018)CrossRefGoogle Scholar
  5. 5.
    Banerjee, B., Bovolo, F., Bhattacharya, A., Bruzzone, L., Chaudhuri, S., Mohan, B.K.: A new self-training-based unsupervised satellite image classification technique using cluster ensemble strategy. IEEE Geosci. Remote Sens. Lett. 12(4), 741–745 (2015)CrossRefGoogle Scholar
  6. 6.
    Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)CrossRefGoogle Scholar
  7. 7.
    Celebi, M.E.: Partitional Clustering Algorithms. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-09259-1zbMATHGoogle Scholar
  8. 8.
    Dave, R.N.: Validating fuzzy partitions obtained through c-shells clustering. Pattern Recogn. Lett. 17(6), 613–623 (1996)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Ganesan, P., Palanivel, K., Sathish, B., Kalist, V., Shaik, K.B.: Performance of fuzzy based clustering algorithms for the segmentation of satellite images–a comparative study. In: 2015 IEEE Seventh National Conference on Computing, Communication and Information Systems (NCCCIS), pp. 23–27. IEEE (2015)Google Scholar
  10. 10.
    Ganesan, P., Sathish, B., Sajiv, G.: A comparative approach of identification and segmentation of forest fire region in high resolution satellite images. In: World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), pp. 1–6. IEEE (2016)Google Scholar
  11. 11.
    Genitha, C.H., Vani, K.: Classification of satellite images using new fuzzy cluster centroid for unsupervised classification algorithm. In: 2013 IEEE Conference on Information & Communication Technologies (ICT), pp. 203–207. IEEE (2013)Google Scholar
  12. 12.
    Gu, J., Jiao, L., Yang, S., Liu, F.: Fuzzy double c-means clustering based on sparse self-representation. IEEE Trans. Fuzzy Syst. 26(2), 612–626 (2017)CrossRefGoogle Scholar
  13. 13.
    Guo, Y., Jiao, L., Wang, S., Wang, S., Liu, F., Hua, W.: Fuzzy-superpixels for polarimetric SAR images classification. IEEE Trans. Fuzzy Syst. 26(5), 2846–2860 (2018)CrossRefGoogle Scholar
  14. 14.
    Haouas, F., Dhiaf, Z.B., Hammouda, A., Solaiman, B.: A new efficient fuzzy cluster validity index: application to images clustering. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2017)Google Scholar
  15. 15.
    Kar, S.A., Kelkar, V.V.: Classification of multispectral satellite images. In: 2013 International Conference on Advances in Technology and Engineering (ICATE), pp. 1–6. IEEE (2013)Google Scholar
  16. 16.
    Kawarkhe, M., Musande, V.: Performance analysis of possisblistic fuzzy clustering and support vector machine in cotton crop classification. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 961–967. IEEE (2014)Google Scholar
  17. 17.
    Poojary, N., D’Souza, H., Puttaswamy, M., Kumar, G.H.: Automatic target detection in hyperspectral image processing: a review of algorithms. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1991–1996. IEEE (2015)Google Scholar
  18. 18.
    Saxena, A., et al.: A review of clustering techniques and developments. Neurocomputing 267, 664–681 (2017)CrossRefGoogle Scholar
  19. 19.
    Shang, R., et al.: A spatial fuzzy clustering algorithm with kernel metric based on immune clone for SAR image segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(4), 1640–1652 (2016)CrossRefGoogle Scholar
  20. 20.
    Villazana, S., Arteaga, F., Seijas, C., Rodriguez, O.: Estudio comparativo entre algoritmos de agrupamiento basado en SVM y c-medios difuso aplicados a señales electrocardiográficas arrítmicas. Rev. INGENIERÍA UC 19(1), 16–24 (2012)Google Scholar
  21. 21.
    Wang, W., Zhang, Y.: On fuzzy cluster validity indices. Fuzzy Sets Syst. 158(19), 2095–2117 (2007)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wu, K.L., Yang, M.S.: A cluster validity index for fuzzy clustering. Pattern Recogn. Lett. 26(9), 1275–1291 (2005)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Xiang, D., Tang, T., Hu, C., Li, Y., Su, Y.: A kernel clustering algorithm with fuzzy factor: application to SAR image segmentation. IEEE Geosci. Remote Sens. Lett. 11(7), 1290–1294 (2014)CrossRefGoogle Scholar
  24. 24.
    Yang, M.S., Nataliani, Y.: Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recogn. 71, 45–59 (2017)CrossRefGoogle Scholar
  25. 25.
    Zhao, Z., Chang, W., Jiang, Y.: Fuzzy local means clustering segmentation algorithm for intensity inhomogeneity image. In: 2015 8th International Congress on Image and Signal Processing (CISP), pp. 453–457. IEEE (2015)Google Scholar
  26. 26.
    Zou, Y., Liu, B.: Survey on clustering-based image segmentation techniques. In: 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 106–110. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad Católica de Trujillo Benedicto XVITrujilloPeru

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