Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images
This article presents the comparative analysis of classification techniques to assign land use and land cover classes from different strategies (pixel-based, object-based, rule-based, distance-based, and neural-based) with a Sentinel-2A satellite image for 2016. The study area is the Sana’a city of...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/9092299 |
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| author | Eman A. Alshari Bharti W. Gawali |
| author_facet | Eman A. Alshari Bharti W. Gawali |
| author_sort | Eman A. Alshari |
| collection | DOAJ |
| description | This article presents the comparative analysis of classification techniques to assign land use and land cover classes from different strategies (pixel-based, object-based, rule-based, distance-based, and neural-based) with a Sentinel-2A satellite image for 2016. The study area is the Sana’a city of Yemen which covers about 18,796.88 km2 land area. This research aims to present the fundamentals of supervised machine learning approaches, including their limitations and strengths and experimentation for twelve classifiers. The outcome of experimentation showed that the Random Forest could be a good choice as a classifier for object-based strategy. In contrast, DTC and SVM were efficient in rule-based and pixel-based strategies. Results also showed that the highest accuracy was with object-based strategy, followed by rule-based and then pixel-based and distance-based strategies. |
| format | Article |
| id | doaj-art-09c97c810370483483dab18a8d4a9aff |
| institution | OA Journals |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-09c97c810370483483dab18a8d4a9aff2025-08-20T02:03:15ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/9092299Analysis of Machine Learning Techniques for Sentinel-2A Satellite ImagesEman A. Alshari0Bharti W. Gawali1Thamar UniversityDr. Babasaheb Ambedkar Marathwada UniversityThis article presents the comparative analysis of classification techniques to assign land use and land cover classes from different strategies (pixel-based, object-based, rule-based, distance-based, and neural-based) with a Sentinel-2A satellite image for 2016. The study area is the Sana’a city of Yemen which covers about 18,796.88 km2 land area. This research aims to present the fundamentals of supervised machine learning approaches, including their limitations and strengths and experimentation for twelve classifiers. The outcome of experimentation showed that the Random Forest could be a good choice as a classifier for object-based strategy. In contrast, DTC and SVM were efficient in rule-based and pixel-based strategies. Results also showed that the highest accuracy was with object-based strategy, followed by rule-based and then pixel-based and distance-based strategies.http://dx.doi.org/10.1155/2022/9092299 |
| spellingShingle | Eman A. Alshari Bharti W. Gawali Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images Journal of Electrical and Computer Engineering |
| title | Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images |
| title_full | Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images |
| title_fullStr | Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images |
| title_full_unstemmed | Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images |
| title_short | Analysis of Machine Learning Techniques for Sentinel-2A Satellite Images |
| title_sort | analysis of machine learning techniques for sentinel 2a satellite images |
| url | http://dx.doi.org/10.1155/2022/9092299 |
| work_keys_str_mv | AT emanaalshari analysisofmachinelearningtechniquesforsentinel2asatelliteimages AT bhartiwgawali analysisofmachinelearningtechniquesforsentinel2asatelliteimages |