Combining the FCM Classifier with Various Kernels to Handle Non-linearity of Class Boundaries

Akshara Preethy Byju (Corresponding Author), Anil Kumar, Alfred Stein, A. Senthil Kumar

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)


This article presents the use of kernel functions in fuzzy classifiers for an efficient land use/land cover mapping. It focuses on handling mixed pixels obtained from a remote sensing image by considering non-linearity between class boundaries. It uses kernel functions combined with the conventional fuzzy c-means (FCM) classifier. Kernel-based fuzzy c-mean classifiers were applied to classify AWiFS and LISS-III images from Resourcesat-1 and Resourcesat-2 satellites. Optimal kernels were obtained from eight single kernel functions. Fractional images generated from high resolution LISS-IV image were used as reference data. Classification accuracy of the FCM classifier increased with 12.93%. Improvement in overall accuracy shows that non-linearity in the dataset was handled adequately. The inverse multiquadratic kernel and the Gaussian kernel with the Euclidean norm were identified as optimal kernels. The study showed that overall classification accuracy of the FCM classifier improved if kernel functions were included.

Original languageEnglish
Pages (from-to)1519-1526
Number of pages8
JournalPhotonirvachak = Journal of the Indian society of remote sensing
Issue number9
Early online date2018
Publication statusPublished - 1 Sep 2018


  • Kernels
  • Feature space
  • Fuzzy error matrix
  • Kernel based fuzzy clustering
  • Classification
  • Fuzzy set


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