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Assessment of Spectral-KMOD Composite Kernel-Based Supervised Noise Clustering Approach in Handling Nonlinear Separation of Classes

  • Ishuita SenGuptaEmail author
  • Anil Kumar
  • Rakesh Kumar Dwivedi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 904)

Abstract

The paper propounds a concept of incorporating composite kernel methods with fuzzy-based image classifiers. The study incorporates noise classifier as a fuzzy classifier. The work demonstrates how nonlinearity among the different classes of remote sensing data with uncertainty is handled with noise classifier without entropy (fuzzy classifier) using composite kernel technique for land use/land cover maps generation. It also showcases the comparative study between the performance of Noise Classifier with Euclidean Distance and Noise Classifier with Composite Kernel functions. This study has incorporated the composition of two prominent kernels: Spectral and KMOD Kernel. The performance of both the classifier is evaluated in supervised mode and, image-to-image assessment of accuracy has been carried out using FERM (Fuzzy Error Matrix).

Keywords

Fuzzy classifier Mixed pixel Composite kernel Noise classifier FERM 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ishuita SenGupta
    • 1
    Email author
  • Anil Kumar
    • 2
  • Rakesh Kumar Dwivedi
    • 1
  1. 1.College of Computing Sciences and Information TechnologyTeerthanker Mahaveer UniversityMoradabadIndia
  2. 2.Photogrammetry and Remote Sensing DepartmentIndian Institute of Remote SensingDehradunIndia

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