Jordanian Journal of Informatics and Computing

ISSN: 3080-6828 (Online)

A Comparative Analysis of Classification Algorithms in Land Cover Mapping: A Study of MLC, Mahalanobis Distance, NNC, and SVM for 2023 and 2018: Case study of Wasit province, central Iraq

by 

Yasir Abdulameer Nayyef Aldabbagh ;

Ali Ibrahim Zghair Alnasrawi

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Published: 2025/08/26

Abstract

The research presents in this paper a comparison between different classification procedures for land cover data between the present and four years prior. The techniques include Maximum Likelihood Classification (MLC), Mahalanobis Distance, Nearest Neighbor Classification (NNC), and Support Vector Machines (SVM). Confusion matrices have been employed to evaluate performance, measuring categorical biases across water, vegetation, urban structures, soil, and other land cover types for each method. The results reveal variations in accuracy not only over time but also across algorithms, offering insights into the strengths and limitations of these classification systems under different temporal conditions. The study aims to lay the foundation for future adaptations of these approaches to improve land cover analysis and long-term monitoring.

Keywords

Classification AlgorithmsLand Cover mappingIraqMaximum Likelihood Classification (MLC)Mahalanobis DistanceNearest Neighbour Classification (NNC)Support Vector Machines (SVM)

A Comparative Analysis of Classification Algorithms in Land Cover Mapping: A Study of MLC, Mahalanobis Distance, NNC, and SVM for 2023 and 2018: Case study of Wasit province, central Iraq is licensed under CC BY 4.0CCBY

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