Machine learning techniques for land use/land cover classification of medium resolution optical satellite imagery focusing on temporary inundated areas

Van Leeuwen Boudewijn and Tobak Zalán and Kovács Ferenc: Machine learning techniques for land use/land cover classification of medium resolution optical satellite imagery focusing on temporary inundated areas. In: Journal of environmental geography, (13) 1-2. pp. 43-52. (2020)

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Abstract

Classification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

Item Type:	Article
Journal or Publication Title:	Journal of environmental geography
Date:	2020
Volume:	13
Number:	1-2
ISSN:	2060-467X
Page Range:	pp. 43-52
Language:	English
Place of Publication:	Szeged
Related URLs:	http://acta.bibl.u-szeged.hu/70901/
DOI:	10.2478/jengeo-2020-0005
Uncontrolled Keywords:	Térinformatika
Additional Information:	Bibliogr.: 52. p. ; ill. ; összefoglalás angol nyelven
Subjects:	01. Natural sciences 01. Natural sciences > 01.05. Earth and related environmental sciences
Date Deposited:	2022. Jun. 24. 13:12
Last Modified:	2022. Jun. 28. 10:44
URI:	http://acta.bibl.u-szeged.hu/id/eprint/76072

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