Analysis on the Effect of Spectral Index Images on Improvement of Classification Accuracy of Landsat-8 OLI Image

  • Magpantay, Abraham T. (College of Computer Studies, Far Eastern University Institute of Technology) ;
  • Adao, Rossana T. (College of Computer Studies, Far Eastern University Institute of Technology) ;
  • Bombasi, Joferson L. (College of Computer Studies, Far Eastern University Institute of Technology) ;
  • Lagman, Ace C. (College of Computer Studies, Far Eastern University Institute of Technology) ;
  • Malasaga, Elisa V. (College of Computer Studies, Far Eastern University Institute of Technology) ;
  • Ye, Chul-Soo (Department of Aviation and IT Convergence, Far East University)
  • Received : 2019.08.05
  • Accepted : 2019.08.21
  • Published : 2019.08.31


In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image.After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.


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