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Journal of the Indian Society of Remote Sensing

, Volume 47, Issue 12, pp 2009–2025 | Cite as

Noise Clustering-Based Hypertangent Kernel Classifier for Satellite Imaging Analysis

  • Achala ShakyaEmail author
  • Anil Kumar
Research Article
  • 39 Downloads

Abstract

The classification accuracy and the computational complexity are degraded by the occurrence of nonlinear data and mixed pixels present in satellite images. Therefore, the kernel-based fuzzy classifiers are required for the separation of linear and nonlinear data. This paper presents two classifiers for handling the nonlinear separable data and mixed pixels. The classifiers, noise clustering (NC) and NC with hypertangent kernels (NCH), are used for handling these problems in the satellite images. In this study, a comparative study between NC and NCH has been carried out. The membership values of KFCM are obtained to produce the final result. It is found that the proposed classifiers achieved good accuracy. It is observed that there is an enhancement in the classification accuracy by using NC and NCH. The maximum accuracy achieved for NC and NCH is 75% at δ = 0.7, δ = 0.5, respectively. After comparing both the results, it has identified that NCH gives better results. The classification of Formosat-2 data is done by obtaining optimized values of m and δ to generate the fractional outputs. The classification accuracy is performed by using the error matrix with the incorporation of hard classifier and α-cut.

Keywords

Kernels Fuzzy c-means Noise clustering Kernel-based fuzzy c-mean Entropy 

Notes

References

  1. Ablin, R., & Sulochna, C. H. (2013). A survey of hyperspectral image classification in remote sensing. International Journal of Advanced Research in Computer and Communication Engineering,2(8), 2986–3000.Google Scholar
  2. Ben-Hur, A., Horn, D., Siegelmann, H. T., & Vapinik, V. (2001). Support vector clustering. Journal of Machine Learning Research,2, 125–137.Google Scholar
  3. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c means clustering algorithm. Computers & Geosciences,10(2–3), 191–203.Google Scholar
  4. 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
  5. 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
  6. Campbell, J. B. (1996). Introduction to Remote Senisng (pp. 337–349). New York: The Guilford Press.Google Scholar
  7. Choodarathnakara, A. L., Kumar, D. T. A., Koliwad, D. S., & Patil, D. C. G. (2012a). Soft classification techniques for RS data. IJCSET,2(11), 1468–1471.Google Scholar
  8. Choodarathnakara, A. L., Kumar, T. A., & Shivaprakash Koliwad, D.C.G. (2012b). Satellite image classification with fuzzy logic: from hard to soft computing situation. International Journal of Computer Science & Applications (TIJCSA), 1(9).Google Scholar
  9. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment,46(1991), 35–46.Google Scholar
  10. Dave, R. N. (1990). Fuzzy shell-clustering and applications to circle detection in digital images. International Journal of General System,16(4), 343–355.Google Scholar
  11. Dave, R. N. (1991). Characterization and detection of noise in clustering. Pattern Recognition Letters,12(11), 657–664.Google Scholar
  12. Dave, R. N., & Krishnapuram, R. (1997). Robust clustering methods: A unified view. IEEE Transactions on Fuzzy Systems,5(2), 270–293.Google Scholar
  13. Filippone, M., Masulli, F., & Rovetta, S. (2010). Applying the possibilistic c-means algorithm in kernel-induced spaces. IEEE Transactions on Fuzzy Systems, 18(3), 572–584.  https://doi.org/10.1109/TFUZZ.2010.2043440.CrossRefGoogle Scholar
  14. Foody, G. M. (1995). Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data. ISPRS Journal of Photogrammetry and Remote Sensing, 50, 2–12.  https://doi.org/10.1016/0924-2716(95)90116-V.CrossRefGoogle Scholar
  15. Foody, G. M. (1997). Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network. Neural Computing & Applications, 5(4), 238–247.Google Scholar
  16. Foody, G. M. (2000). Estimation of sub-pixel land cover composition in the presence of untrained classes. Computers and Geosciences, 26(4), 469–478.  https://doi.org/10.1016/S0098-3004(99)00125-9.CrossRefGoogle Scholar
  17. Franklin, S. E., Peddle, D. R., Dechka, J. A., & Stenhouse, G. B. (2002). Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bear habitat mapping. International Journal of Remote Sensing,23(21), 4633–4652.Google Scholar
  18. Gallego, F. J. (2004). Remote sensing and land cover area estimation. International Journal of Remote Sensing,25(15), 3019–3047.Google Scholar
  19. Girolami, M., & Tr, S. (2002). Mercer kernel based clustering in feature space. IEEE Transactions on Neural Networks,13(3), 780–784.Google Scholar
  20. Gong, P., & Howarth, P. J. (1992). Frequency-based contextual classification and gray-level vector reduction for land-use identification. Photogrammetric Engineering and Remote Sensing,58(4), 423–437.Google Scholar
  21. Gopinath, R. A. (1998). Maximum likelihood modeling with Gaussian distributions for classification. In Proceedings international conference on speech signal processing (pp. 661–664). WA: Seattle.Google Scholar
  22. Graves, D., & Pedrycz, W. (2007). Fuzzy c-means, gustafson-kessel fcm, and kernel-based fcm: A comparative study. In Analysis and design of intelligent systems using soft computing techniques (pp. 140–149). Berlin, Heidelberg: Springer.Google Scholar
  23. Harikumar, A. (2014). Effect of discontinuity adaptive MRF models with noise classifier. University of Twente, Enschede, The Netherlands & Indian Institute of Remote Sensing, ISRO, Dehradhun, India, published M. Sc Thesis.Google Scholar
  24. Hepner, G., Logan, T., Ritter, N., & Bryant, N. (1990). Artificial neural network classification using a minimal training set-Comparison to conventional supervised classification. Photogrammetric Engineering and Remote Sensing, 56(4), 469–473.Google Scholar
  25. Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing,23(4), 725–749.Google Scholar
  26. 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
  27. 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
  28. Kainz, W. (2007). Fuzzy Logic and GIS. University of Vienna, Austria, Retrieved from https://homepage.univie.ac.at/wolfgang.kainz/Lehrveranstaltungen/ESRI_Fuzzy_Logic/File_2_Kainz_Text.pdf.
  29. 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
  30. Kavzoglu, T., & Reis, S. (2008). Performance analysis of maximum likelihood and artificial neural network classifiers for training sets with mixed pixels. GIScience & Remote Sensing, 45(3), 330–342.Google Scholar
  31. Kohram, M., & Sap, M. N. M. (2008). Composite kernels for support vector classification of hyper-spectral data. Mexican International Conference on Artificial Intelligence (pp. 360–370). Berlin, Heidelberg: Springer.Google Scholar
  32. Kontoes, C. C., & Rokos, D. (1996). The integration of spatial context information in an experimental knowledge-based system and the supervised relaxation algorithm—Two successful approaches to improving SPOT-XS classification. Remote Sensing,17(16), 3093–3106.Google Scholar
  33. Kreinovich, V. (2013). Membership functions or α-cuts? algorithmic (constructivist) analysis justifies an interval approach. Applied Mathematical Sciences, 7(5), 217–228.Google Scholar
  34. Krishnapuram, R., & Keller, J. M. (1993). A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1(2), 98–110.  https://doi.org/10.1109/91.227387.CrossRefGoogle Scholar
  35. Krishnapuram, R., & Keller, J. M. (1996). The possibilistic C-means algorithm: Insights and recommendations. IEEE Transactions on Fuzzy Systems,4(3), 385–393.  https://doi.org/10.1109/91.531779.CrossRefGoogle Scholar
  36. Kumar, A. (2007). Investigation in sub-pixel classification approaches for land use and land cover mapping. New York: IIT Roorkee.Google Scholar
  37. Kumar, A., Ghosh, S. K., Dadhwal, V. K., Function, M. K., Estimation, D., & Matrix, F. E. (2005). Study off mixed kernel effect on classification accuracy (pp. 1–4).Google Scholar
  38. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation. New York: Wiley.Google Scholar
  39. Lippmann, R. P. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine,4(2), 4–22.Google Scholar
  40. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing,28(5), 823–870.  https://doi.org/10.1080/01431160600746456.CrossRefGoogle Scholar
  41. Okeke, F., & Karnieli, A. (2006). Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analyses. Part I: Algorithm development. International Journal of Remote Sensing, 1–2 (December 2014), 153–176.Google Scholar
  42. Mercier, G., & Lennon, M. (2003). Support vector machines for hyperspectral image classification with spectral-based kernels. In IGARSS (pp. 288–290).Google Scholar
  43. 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
  44. Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation,12, S27–S31.Google Scholar
  45. Patra, S., & Bruzzone, L. (2010). A fast cluster-assumption based active-learning technique for classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing,49(5), 1617–1626.Google Scholar
  46. Ponce-Cruz, P., & Ramírez-Figueroa, F. D. (2009). Intelligent Control Systems with LabVIEW™. Springer, London.  https://doi.org/10.1007/978-1-84882-684-7.Google Scholar
  47. Ponce-Cruz, P., & Ramírez-Figueroa, F. D. (2010). Fuzzy Logic. Intelligent Control Systems with LabVIEW™ (pp. 9–46). London: Springer.  https://doi.org/10.1007/978-1-84882-684-7_2.CrossRefGoogle Scholar
  48. Pontius, R. G, Jr., & Cheuk, M. L. (2006). A generalized crosstabulation matrix to compare soft-classified maps at multiple resolutions. International Journal of Geographical Information Science, 20, 1–30.Google Scholar
  49. Reznik, L., & Stoica, A. (1994). Mapping alpha-cut borders: classification and PID realization. In Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 3, 1604–1607.Google Scholar
  50. Ricotta, C., & Avena, G. C. (2002). Evaluating the degree of fuzziness of thematic maps with a generalized entropy function: a methodological outlook. International Journal of Remote Sensing, 23, 4519–4523.  https://doi.org/10.1080/01431160210153985.CrossRefGoogle Scholar
  51. Rokach, L., & Maimon, O. (2005). Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews,35(4), 476–487.Google Scholar
  52. Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man and Cybernetics,21, 660–674.Google Scholar
  53. San Miguel-Ayanz, J., & Biging, G. S. (1997). Comparison of single-stage and multi-stage classification approaches for cover type mapping with TM and SPOT data. Remote Sensing of Environment,59(1), 92–104.Google Scholar
  54. SenGupta, I., Kumar, A., & Dwivedi, R. K. (2019). Performance evaluation of kernel-based supervised noise clustering approach. Journal of the Indian Society of Remote Sensing, 47(2), 317–330.Google Scholar
  55. Silvan-Cardenas, J. L., & Wang, L. (2008). Sub-pixel confusion-uncertainty matrix for assessing soft classifications. Remote Sensing of Environment, 112(3), 1081–1095.  https://doi.org/10.1016/j.rse.2007.07.017.CrossRefGoogle Scholar
  56. Singha, M. R. I. N. A. L. (2013). Study the effect of discontinuity adaptive MRF models in fuzzy based classifier. Enschede: University of Twente Faculty of Geo-Information and Earth Observation (ITC).Google Scholar
  57. Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment,62(1), 77–89.Google Scholar
  58. Stuckens, J., Coppin, P. R., & Bauer, M. E. (2000). Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sensing of Environment,71(3), 282–296.Google Scholar
  59. Suganya, R., & Shanthi, R. (2012). Fuzzy c-means algorithm—A review. International Journal of Scientific and Research Publications,2(11), 1–3.Google Scholar
  60. Tan, K. C., Lim, H. S., & Jafri, M. Z. M. (2011). Comparison of neural network and maximum likelihood classifiers for land cover classification using landsat multispectral data. In IEEE Conference on Open Systems (pp. 241–244).Google Scholar
  61. Tso, B., & Mather, P. M. (2000). Classification of remotely sensed data (pp. 54–61). Boca Raton: CRC Press.Google Scholar
  62. Yang, A., Jiang, L., & Zhou, Y. (2007). A KFCM-based fuzzy classifier. In 4th international conference on fuzzy systems and knowledge discovery (FSKD 2007) (Fskd) (pp. 80–84).Google Scholar
  63. Zadeh, L. A. (1965). Fuzzy sets. Information and Control,8(3), 338–353.Google Scholar
  64. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28.  https://doi.org/10.1016/0165-0114(78)90029-5.CrossRefGoogle Scholar
  65. Zhang, D., & Chen, S. (2002). Fuzzy clustering using kernel method. In International conference on control and automation (pp. 123–127).  https://doi.org/10.1109/icca.4132002.1229535.
  66. Zhang, D.-Q., & Chen, S.-C. (2003). Clustering incomplete data using kernel-based fuzzy c-means algorithm. Neural Processing Letters,18(3), 155–162.Google Scholar
  67. Zhang, J., & Foody, G. M. (1998). A fuzzy classification of sub-urban land cover from remotely sensed imagery. International Journal of Remote Sensing,19, 2721–2738.Google Scholar
  68. Zhang, J., & Foody, G. M. (2001). 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(4), 615–628.  https://doi.org/10.1080/01431160050505883.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Computer Engineering DepartmentBanasthali VidyapithBanasthaliIndia
  2. 2.Photogrammetry and Remote Sensing Department, Scientist/ Engineer ‘SG’Indian Institute of Remote SensingDehradunIndia

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