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

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.