Reseach Article
A Subspace KNN Ensemble Classifier for Land Cover Classification using Medium Resolution Satellite Images
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 3 |
| Year of Publication: 2024 |
| Authors: Atijosan Abimbola, Olaoluwa Idayat A. |
10.5120/jaai202415
|
Atijosan Abimbola, Olaoluwa Idayat A. . A Subspace KNN Ensemble Classifier for Land Cover Classification using Medium Resolution Satellite Images. Journal of Advanced Artificial Intelligence. 1, 3 ( Dec 2024), 23-30. DOI=10.5120/jaai202415
Abstract
Accurate land cover information is crucial for the development and implementation of various environmental, socio-political and economic policies. Classification techniques are fundamental in this regard, as they determine the accuracy of information obtained from land cover classification and thus, affect the accuracy of subsequent applications. In this research, a subspace KNN ensemble classifier with a nearest neighbour learning algorithm is proposed for the accurate classification of medium-resolution multispectral satellite images. The Landsat satellite dataset obtained from the UC Irvine machine learning repository was used as the testing data. For performance evaluation, confusion matrix and receiver operating curve plots were used for performance evaluation. Performance comparison was made with three well-known machine learning classifiers namely, Decision Tree (DT), Support Vector Machine (SVM) and Kernel Naïve Bayes (KNB) models to determine the model with the highest accuracy. Results obtained show that the subspace KNN ensemble classifier outperforms the other classifiers in terms of accuracy as it achieves a 91.5% accuracy while DT, SVM and KNB classifiers achieved 85%, 90.4 and 81.8% accuracy respectively.
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