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

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Abstract

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.

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Authors and Affiliations

  1. Department of Geo-Informatics, Indian Institute of Remote Sensing, 4 Kalidas Road, Dehra Dun, Uttarakhand, 248001, India

    Akshara Preethy Byju

  2. Photogrammetry and Remote Sensing Department, Indian Institute of Remote Sensing, 4 Kalidas Road, Dehra Dun, Uttarakhand, 248001, India

    Anil Kumar

  3. Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands

    Alfred Stein

  4. Indian Institute of Remote Sensing (IIRS), Dehra Dun, Uttarakhand, 248001, India

    A. Senthil Kumar

Authors
  1. Akshara Preethy Byju
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  2. Anil Kumar
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  3. Alfred Stein
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  4. A. Senthil Kumar
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Correspondence to Akshara Preethy Byju.

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Byju, A.P., Kumar, A., Stein, A. et al. Combining the FCM Classifier with Various Kernels to Handle Non-linearity of Class Boundaries. J Indian Soc Remote Sens 46, 1519–1526 (2018). https://doi.org/10.1007/s12524-018-0813-z

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  • DOI: https://doi.org/10.1007/s12524-018-0813-z

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