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Research Article Open access CC BY 4.0

Comparing Machine Learning Algorithms in Land Use Land Cover Classification of Landsat 8 (OLI) Imagery

O. J. Aigbokhan, O. J. Pelemo, O. M. Ogoliegbune, N. E. Essien, A. A. Ekundayo, S. I. Adamu

Asian Research Journal of Mathematics · pp. 62–74 · Published 9 Apr 2022

10.9734/arjom/2022/v18i330367

Abstract

In recent times, there have been increased rates at which researchers are searching for advanced ways of carrying out land-use land-cover (LULC) mapping, especially in developing countries. Four machine-learning algorithms, namely Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbour (K-NN), and Gaussian Mixture Models (GMM) were examined. This study also attempted to validate the various models using the index-based validation method. Accuracy assessment was performed by using the Kappa coefficient. The results of the LULC showed that RF classified 23% of the study area as bare land, SVM has 24% of the study area classified as bare land, K-NN also allotted 24% to bare land, while that of GMM classifier was 30%. The overall accuracy of RF, SVM, K-NN and GMM were 0.9840, 0.9780, 0.9641 and 0.9421 respectively. The Kappa Coefficient of the various classifiers were RF (0.9695), SVM (0.9580), K-NN (0.9319) and GMM (0.8916). This study showed that though all the algorithms performed relatively very well, RF performed better than the other classifiers. It suffices to state that, there is a need for further studies since other extraneous environmental variables may be underpinning these conclusions.

Supervised machine learning algorithm kappa coefficient classification

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