Open AccessComparing Machine Learning Algorithms in Land Use Land Cover Classification of Landsat 8 (OLI) Imagery
Author(s) -
O. J. Aigbokhan,
Oluwole John Pelemo,
O. M. Ogoliegbune,
N. E. Essien,
A. A. Ekundayo,
Shamsuddeen Adamu
Publication year - 2022
Publication title -
asian research journal of mathematics
Language(s) - English
Resource type - Journals
ISSN - 2456-477X
DOI - 10.9734/arjom/2022/v18i330367
Subject(s) - land cover , random forest , support vector machine , cohen's kappa , artificial intelligence , classifier (uml) , machine learning , computer science , kappa , pattern recognition (psychology) , mixture model , gaussian , algorithm , land use , mathematics , engineering , chemistry , civil engineering , geometry , computational chemistry
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.