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

• Autorzy: Alwan Imzahim A. , Aziz Nadia A.

Identyfikatory

DOI

10.7494/geom.2021.15.1.5

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

land cover mapping; Sentinel 2; supervised classification; maximum likelihood; support vector machine; SVM; confusion matrix

PL

mapowanie pokrycia terenu; Sentinel 2; klasyfikacja nadzorowana; maksymalne prawdopodobieństwo; maszyna wektorów wspomagających (SVM); macierz konfuzji

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2021
• Tom: Vol. 15, no. 1
• Strony: 5--21
• Opis fizyczny: Bibliogr. 46 poz., rys., tab.

Twórcy

autor

Alwan Imzahim A.

• University of Technology, Civil Engineering Department, Baghdad, Iraq

• https://orcid.org/0000-0002-8128-6658

autor

Aziz Nadia A.

• Ministry of Science and Technology, Directorate of Space and Communication, Baghdad, Iraq

• https://orcid.org/0000-0003-1978-7432

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)