The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis
Helmi Zulhaidi Mohd Shafri, Affendi Suhaili & Shattri Mansor
Journal of Computer Science, Volume 3, Issue 6, pp. 419–423 · 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.
Licence and reuse
Free to read. This article carries the publisher’s copyright, as printed on it: © 2007 Science Publications. It is free to read; ask us before reusing it.
How to reuse this articleResponses (0)
About this article
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
- Licence
- © Science Publications
- Issue
- Volume 3, Issue 6
- Policies
- Editorial independenceUse of generative AIData availabilityAppeals and complaints
- Version
- Version of record