Geomatics and Environmental Engineering, vol. 15, no. 1

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Vol. 15 No. 1 (2021): Geomatics and Environmental Engineering

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Submitted
Jul 15, 2020
Accepted
Sep 8, 2020
Published
Feb 4, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq

https://doi.org/10.7494/geom.2021.15.1.5
Nadia A. Aziz
Ministry of Science and Technology, Directorate of Space and Communication, Baghdad 10070, Iraq
https://orcid.org/0000-0003-1978-7432
Imzahim A. Alwan
University of Technology, Civil Engineering Department, Baghdad 10066, Iraq
https://orcid.org/0000-0002-8128-6658

Corresponding Author(s) : Nadia A. Aziz

nadia_naa@yahoo.com

Geomatics and Environmental Engineering, Vol. 15 No. 1 (2021): Geomatics and Environmental Engineering

Abstract

Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al‑Hawizeh marsh/Iraq‑Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built‑up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.

Keywords

land cover maping Sentinel 2 supervised clasification maximum likehood Support vector machine (SVM) confusion matrix
Aziz, N. A., & Alwan, I. A. (2021). An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq. Geomatics and Environmental Engineering, 15(1), 5–21. https://doi.org/10.7494/geom.2021.15.1.5
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