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Comparing Pan-sharpened Landsat-9 and Sentinel-2 for Land-Use Classification Using Machine Learning Classifiers

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Abstract

This paper evaluates the ability of two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to generate land-use maps using the recently launched Landsat-9 and Sentinel-2, two of the most used and popular satellite imagery sources. The potential to improve Landsat-9 performance was tested by pan-sharpening different bands of high-resolution data (15 m). For optimal performance of both classifiers, model tuning methods were applied by trying different combinations of key parameters of each model. This comparison was made in two different areas in Central Morocco. The results show that SVM performs slightly better than RF in classifying two images. In addition, Sentinel-2 exhibits significant multivariety classification ability compared to the pan-sharpened Landsat-9, despite the improved resolution of the latter. Lastly, the best classification performances were recorded for the combination Sentinel-2/SVM classifier. At last, machine learning algorithms prove their efficiency in classifying satellite images with high performance.

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Authors and Affiliations

  1. National Institute of Agricultural Research (INRA), CRRA, Tadla, Morocco

    Yassine Bouslihim

  2. International Water Research Institute (IWRI), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco

    Yassine Bouslihim, Mohamed Hakim Kharrou, Taha Attou, Lhoussaine Bouchaou & Abdelghani Chehbouni

  3. Faculty of Sciences and Techniques, Laboratory Physico-Chemistry of Processes and Materials, Hassan First University of Settat, Settat, Morocco

    Abdelhalim Miftah

  4. Faculty of Science and Techniques, Laboratory of Intelligent Systems Georesources and Renewable Energies, Sidi Mohamed Ben Abdellah University, Fes, Morocco

    Abdelhalim Miftah

  5. Laboratory of Applied Geology and Geo-Environment, Ibn Zohr University, 80035, Agadir, Morocco

    Lhoussaine Bouchaou

  6. Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco

    Abdelghani Chehbouni

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  1. Yassine Bouslihim
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Correspondence to Yassine Bouslihim.

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Bouslihim, Y., Kharrou, M.H., Miftah, A. et al. Comparing Pan-sharpened Landsat-9 and Sentinel-2 for Land-Use Classification Using Machine Learning Classifiers. J geovis spat anal 6, 35 (2022). https://doi.org/10.1007/s41651-022-00130-0

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