Performance Evaluation of Kernel-Based Supervised Noise Clustering Approach

  • Ishuita SenGuptaEmail author
  • Anil Kumar
  • Rakesh Kumar Dwivedi
Research Article


Remote sensing data have been used for effective recognition and classification of land use and land cover features on Earth surface. This paper presents a new approach of handling mixed pixel problem present in remote sensing data and nonlinearity between class boundaries through incorporating the kernel functions with noise classifier (NC). Kernel functions have been combined with conventional noise clustering without entropy, classification method (KNC) to classify data obtained from Landsat-8 and Formosat-2 satellites. Simulated image technique has been introduced and used to handle mixed pixel problem and to assess the results of adopted classification method (KNC). The procedure includes optimization of parameters for nine different kernels, finding the best performing kernel, and image to image accuracy assessment using fuzzy error matrix. A comparative analysis of kernel-based noise classifier over conventional NC classifier has also been included in this paper.


Remote sensing Kernel functions Kernel-based noise clustering without entropy Fuzzy error matrix 


  1. Awan, A. M., & Sap, M. N. M. (2005). Clustering spatial data using a kernel-based algorithm. In Proceedings of the Annual Research Seminar (pp. 306–310).Google Scholar
  2. Ayat, N. E., Cheriet, M., Remaki, L., & Suen, C. Y. (2001). KMOD—a new support vector machine kernel with moderate decreasing. In Document analysis and recognition, 2001. Proceedings, 6th international conference (pp. 1215–1219).Google Scholar
  3. Ben-Hur, A., Horn, D., Siegelmann, H. T., & Vapinik, V. (2001). Support vector clustering. Journal of Machine Learning Research, 2, 125–137.Google Scholar
  4. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2–3), 191–203.Google Scholar
  5. Bhatt, S. R., & Mishra, P. K. (2013). Study of local kernel with fuzzy c-mean algorithm. International Journal of Advanced Research in Computer and Software Engineering, 3(12), 636–639.Google Scholar
  6. Binaghi, E., Brivio, P. A., Ghezzi, P., & Rampini, A. (1999). A fuzzy set-based accuracy assessment of soft classification. Pattern Recognition Letters, 20(9), 935–948.Google Scholar
  7. Byju, A. P. (2015). Non-linear separation of classes using a kernel based fuzzy c-means (KFCM) approach. M.Sc. thesis, ITC, University of Twente.Google Scholar
  8. Camps-Valls, G., & Bruzzone, L. (2009). Kernel methods for remote sensing data analysis (pp. 25–45). ISBN:978-0-470-72211-4.Google Scholar
  9. Choodarathnakara, A. L., Kumar, D. T. A., Koliwad, D. S., & Patil, D. C. G. (2012). Soft classification techniques for RS data. IJCSET, 2(11), 1468–1471.Google Scholar
  10. Chotiwattana, W. (2009). Noise clustering algorithm based on kernel method. In Advance computing conference. IACC 2009 (pp. 56–60). IEEE.Google Scholar
  11. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.Google Scholar
  12. Dave, R. N. (1991). Characterization and detection of noise in clustering. Pattern Recognition Letters, 12, 657–664.Google Scholar
  13. Dave, R. N. (1993). Robust fuzzy clustering algorithms. Fuzzy Systems. In 2nd IEEE international conference on 1993. IEEE (pp. 1281–1286).Google Scholar
  14. Dave, R. N., & Krishnapuram, R. (1997). Robust clustering methods: a unified view. IEEE Transactions on Fuzzy Systems, 5, 270–293.Google Scholar
  15. Dave, R., & Sen, S. (1997). Noise clustering algorithm revisited. In Fuzzy information processing society, 1997. NAFIPS’97. Annual meeting of the North American, IEEE (pp. 199–204).Google Scholar
  16. Dwivedi, R. K., Ghosh, S. K., & Kumar, Anil. (2012a). Study of fuzzy based classifier parameter across spatial resolution. International Journal of Computer Applications, 50(11), 17–24.Google Scholar
  17. Dwivedi, R. K., Ghosh, S. K., & Kumar, A. (2012b). Study of fuzzy based classifier parameter using fuzzy matrix. International Journal of Soft Computing and Engineering, 2(3), 358–365.Google Scholar
  18. Fisher, P. F., & Pathirana, S. (1990). The evaluation of fuzzy membership of land cover classes in the suburban zone. Remote Sensing of Environment, 34, 121–132.Google Scholar
  19. Foody, G. M. (1996a). Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data. International Journal of Remote Sensing, 17, 1317–1340. Scholar
  20. Foody, G. M. (1996b). Relating the land-cover composition of mixed pixels to artificial neural network classification output. Photogrammetric Engineering and Remote Sensing, 62, 491–499.Google Scholar
  21. Foody, G. M. (2000). Estimation of sub-pixel land cover composition in the presence of untrained classes. Computers and Geosciences, 26, 469–478.Google Scholar
  22. Ganesan, P., & Rajini, V. (2010). A method to segment color images based on modified fuzzy-possibilistic-c-means clustering algorithm. In Recent advances in space technology services and climate change 2010 (RSTS and CC-2010) (pp. 157–163).
  23. Girolami, M. (2002). Mercer Kernel Based Clustering in Feature Space. IEEE Transactions on Neural Networks, 13(3), 780–784.Google Scholar
  24. Green, K., & Congalton, R. G. (2004). An error matrix approach to fuzzy accuracy assessment. The NIMA Geocover Project. Boca Raton, FL: CRC Press.Google Scholar
  25. Harikumar, A. (2014). The effects of discontinuity adaptive MRF models on the noise classifier. M.Sc. thesis, ITC, University of Twente, The Netherlands.Google Scholar
  26. Hathaway, R. J., Bezdek, J. C., & Pedrycz, W. (1996). A parametric model for fusing heterogeneous fuzzy data. IEEE Transactions on Fuzzy Systems, 4, 1277–1282.Google Scholar
  27. Hofmann, T., Scholkopf, B., & Smola, A. J. (2008). Kernel methods in machine learning. The Annals of Statistics, 36(3), 1171–1220.Google Scholar
  28. Hu, Y., Zuo, C., Qu, F., & Shi, W. (2012). Unsupervised possibilistic clustering based on kernel methods. Physics Procedia, 25, 1084–1090. Scholar
  29. Huang, H., Chuang, Y., & Chen, C. (2011). Multiple kernel fuzzy clustering. IEEE Transactions on Fuzzy Systems, 20, 1–15.Google Scholar
  30. Ibrahim, M. A., Arora, M. K., & Ghosh, S. K. (2005). Estimating and accommodating uncertainty through the soft classification of remote sensing data. International Journal of Remote Sensing, 26, 2995–3007. Scholar
  31. Isaacs, J. C., Foo, S. Y., & Meyer-baese, A. (2007). Novel kernels and kernel PCA for pattern recognition. In Proceedings of 2007 IEEE symposium on computer intelligence in robotics and automation (pp. 438–443).Google Scholar
  32. Jain, C., & Srivastava, G. (2013). Designing a classifier with KFCM algorithm to achieve optimization of clustering and classification simultaneously. International Journal of Emerging Technology and Advanced Engineering, 3(9), 131–140.Google Scholar
  33. Jolion, J. M., & Rosenfeld, A. (1989). Cluster detection in background noise. Pattern Recognition, 22, 603–607.Google Scholar
  34. Kandpal, N. (2016). Non-linear separation of classes using a kernel based possibilistic c-means. M.Sc. thesis, ITC, University of Twente, The Netherlands.Google Scholar
  35. Kaur, P., Gupta, P., & Sharma, P. (2012). Review and comparison of kernel based fuzzy image segmentation techniques. I.J. Intelligent Systems and Applications, 7, 50–60.Google Scholar
  36. Krishnapram, R., & Feg, C. P. (1992). Fitting an unknown number of lines and planes to image data through compatible cluster merging. Pattern Recognition, 25, 385–400.Google Scholar
  37. Krishnapuram, R., & Keller, J. M. (1996). The possibilistic c-means algorithm: Insights and recommendations. IEEE Transactions on Fuzzy Systems, 4, 385–393.Google Scholar
  38. Kumar, A. (2007). Investigation in sub-pixel classification approaches for land use and land cover mapping. New York: IIT Roorkee.Google Scholar
  39. Kumar, A., Ghosh, S. K., Dadhwal, V. K., Function, M. K., Estimation, D., & Matrix, F. E. (2005). Study of the mixed kernel effect classification accuracy. Proceedings of the ISPRS Commission VII Symposium, 36(7), 2–5.Google Scholar
  40. Lin, H., & Lin, C. (2003). A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Department of Computer Science and Information Engineering, National Taiwan University, Taiwan.Google Scholar
  41. Lillesand, T. M., & Kiefer, R. W. (1979). Thematic information extraction: Pattern recognition. In Remote sensing and image interpretation. John Wiley and Sons, New York.Google Scholar
  42. Liu, L., Yang, A., Zhou, W., Zhang, X., Fei, M., & Tu, X. (2015). Robust dataset classification approach based on neighbor searching and kernel fuzzy c-means. IEEE/CAA Journal of Automatica Sinica, 2(3), 235–247. Scholar
  43. Mercier, G., & Lennon, M. (2003). Support vector machines for hyperspectral image classification with spectral-based kernels. In Geoscience and remote sensing symposium, 2003. IGARSS’03. Proceedings. 2003 IEEE international (pp. 288–290).
  44. Mittal, D., & Tripathy, B. K. (2015). Efficiency analysis of kernel functions in uncertainty based c-means algorithms. In International conference on advances in computing, communications and informatics (ICACCI) (pp. 807–813).Google Scholar
  45. Miyamoto, S., Ichihashi, H., & Honda, K. (2008). Algorithms for fuzzy clustering: methods in c-means clustering with applications (pp. 46–66). Berlin: Springer.Google Scholar
  46. Mohamed, R. M., & Farag, A. A. (2004). Mean field theory for density estimation using support vector machines. Computer Vision and Image Processing Laboratory. Louisville, KY: University of Louisville, 40292.Google Scholar
  47. Pal, N. R., & Sarkar, K. (2014). What and when can we gain from the kernel versions of c-means algorithm? IEEE Transactions on Fuzzy Systems, 22(2), 363–379. Scholar
  48. Pontius, R. G., Jr., & Cheuk, M. L. (2006). A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. International Journal of Geographical Information Science, 20, 1–30.Google Scholar
  49. Ravindraiah, R., & Tejaswini, K. (2013). A Survey of Image Segmentation Algorithms Based On Fuzzy Clustering. International Journal of Computer Science and Mobile Computing, 2(7), 200–206.Google Scholar
  50. Rhee, F., Choi, K., & Choi, B. (2012). Kernel approach to possibilistic c-means clustering. International Journal of Intelligent Systems, 24(3), 272–292. Scholar
  51. Richards, J. A. (2012). Remote sensing digital image analysis: An introduction. Berlin: Springer.Google Scholar
  52. Sengar, S. S., Kumar, A., Ghosh, S. K., & Wason, H. R. (2012a). Earthquake induced built-up damage identification using IRS-P6 data—A comparative study using fuzzy based classifiers. Geocarto International. Scholar
  53. Sengar, S. S., Kumar, A., Ghosh, S. K., Wason, H. R., Raju, P. L. N., & Krishnamurthy, Y. V. N. (2012b). Earthquake induced built-up damage identification using fuzzy approach. Geomatics, Natural Hazards and Risk. Scholar
  54. Shalan, M. A., Arora, M. K., & Ghosh, S. K. (2003). An evaluation of fuzzy classifications from IRS 1C LISS III imagery: A case study. International Journal of Remote Sensing, 24, 3179–3186. Scholar
  55. Tsai, D., & Lin, C. (2011). Fuzzy c-means based clustering for linearly and nonlinearly separable data. Pattern Recognition, 44, 1750–1760.Google Scholar
  56. Tso, B., & Mather, P. M. (2000). Classification of remotely sensed data. Boca Raton: CRC Press.Google Scholar
  57. Upadhay, P., Ghosh S. K., Kumar A., Krishna Murthy, Y. V. N., & Raju, P. L. N. (2014). Moist deciduous forest identification using MODIS temporal indices data. International Journal of Remote Sensing, 35(9), 3177–3196 (ISSN 0143-1161 and 1366-5901).Google Scholar
  58. Upadhyay, P., Ghosh, S. K., & Kumar, A. (2013). Moist deciduous forest identification using temporal MODIS data—a comparative study using fuzzy based classifiers. Ecological Informatics, 18, 117–130.Google Scholar
  59. Wu, X.-H. (2006). Noise clustering using a new distance. In IEEE 2006 (pp. 1015–1020).Google Scholar
  60. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 100, 9–34.Google Scholar
  61. Zhang, D., & Chen, S. (2002). Fuzzy clustering using kernel method. In International conference on control and automation (pp. 123–127).
  62. Zhang, J., & Foody, G. (2002). Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches. International Journal of Remote Sensing, 22(5), 615–628.Google Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.College of Computing Sciences and Information TechnologyTeerthanker Mahaveer UniversityMoradabadIndia
  2. 2.MoradabadIndia
  3. 3.Photogrammetry and Remote Sensing Department, Scientist/Engineer ‘SG’Indian Institute of Remote SensingDehradunIndia

Personalised recommendations