Research Article Open Access

The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis

Helmi Zulhaidi Mohd Shafri1, Affendi Suhaili2 and Shattri Mansor2
  • 1 , Afganistan
  • 2 ,
Journal of Computer Science
Volume 3 No. 6, 2007, 419-423

DOI: https://doi.org/10.3844/jcssp.2007.419.423

Submitted On: 23 April 2007
Published On: 30 June 2007

How to Cite: Shafri, H. Z. M., Suhaili, A. & Mansor, S. (2007). The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis . Journal of Computer Science, 3(6), 419-423. https://doi.org/10.3844/jcssp.2007.419.423

Abstract

Several classification algorithms for pattern recognition had been tested in the mapping of tropical forest cover using airborne hyperspectral data. Results from the use of Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Artificial Neural Network (ANN) and Decision Tree (DT) classifiers were compared and evaluated. It was found that ML performed the best followed by ANN, DT and SAM with accuracies of 86%, 84%, 51% and 49% respectively.

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Keywords

  • Remote sensing
  • algorithm
  • accuracy assessment
  • artificial intelligence