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Multiple Kernel Based Remote Sensing Vegetation Classifier with Levy Optimized Subspace

  • V. Shenbaga PriyaEmail author
  • D. Ramyachitra
Article
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Abstract

Vegetation classification in remote sensing (RS) applications details a rich source of information on land use/land cover decision making. To classify the mixed land-cover area of diverse categories through RS imagery, robust classification methods and their techniques act as a substratum for thematic interpretation. Though many classification techniques have been raised for the study of remote sensing images, support vector machine (SVM) has received huge attention. In order to handle non-linear separable high dimensional feature space, optimized subspace-based classifier acts as a bedrock for improved accuracy as accuracy is still a primary concern to design efficient classifiers for nonlinear separable feature space. In this work, a classifier Enhanced Entropy based Multiple Kernel Support Vector Machine with Levy optimized subspace has experimented for vegetation classification. Since SVMs are a typically linear methods, they can be easily derived into non-linear decision criteria by substituting the inner products with kernel functions. The results indicate that the proposed method is getting better accuracy than the existing Methods.

Keywords

Remote sensing Vegetation classification Feature subspace Support Vector Machine Levy optimized subspace Enhanced Entropy based Multiple Kernel Support Vector Machine (EEMK-SVM) classifier 

Notes

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Applications, School of Computer, Information and Mathematical SciencesB. S. Abdur Rahman Crescent Institute Of Science And TechnologyChennaiIndia
  2. 2.Department of Computer Science, School of Computer Science and EngineeringBharathiar UniversityCoimbatoreIndia

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