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Machine Learning for Enhancement Land Cover and Crop Types Classification

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Machine Learning Paradigms: Theory and Application

Part of the book series: Studies in Computational Intelligence ((SCI,volume 801))

Abstract

Big data collected from remote sensing satellites is creating new opportunities for modern development. Remote sensing big data are very complex in terms of their structural, spectral, and textual features according to various satellites generating them. Investigating the character of remote sensed big data becomes an essential need. Land cover and crop types classification are of great importance for monitoring agricultural production and land-use patterns. Many classification approaches have used different parameters settings. In this chapter, we investigate the modern classifiers using the most effective parameters to enhance the classification accuracy of the major crops and land covers that exist in Sentinel-2 satellite images for Fayoum Region of Egypt. Many crop types and major land-cover types are classified according to the Egypt region. This chapter investigates the k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) supervised classifiers. The experimental results show that the SVM and the RF report more robust results. The k-NN reports the least accuracy especially for crop types. The RT, k-NN, ANN, and SVM record 92.7%, 92%, 92.1% and 94.4% respectively. The SVM classifier out-performs the k-NN, ANN and RF classifiers.

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Acknowledgements

This work was supported in part by the GEF/World Bank Project “Regional Co-ordination for Improved Water Resources Management and Capacity Building” alongside the National Authority for Remote, Sensing and Space Science, Egypt.

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Authors and Affiliations

  1. Data Reception and Analysis Division, National Authority for Remote Sensing and Space Science, Cairo, Egypt

    Noureldin Laban & Bassam Abdellatif

  2. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Hala M. Ebeid, Howida A. Shedeed & Mohamed F. Tolba

Authors
  1. Noureldin Laban
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  2. Bassam Abdellatif
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  3. Hala M. Ebeid
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  4. Howida A. Shedeed
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  5. Mohamed F. Tolba
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Corresponding author

Correspondence to Noureldin Laban .

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Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Laban, N., Abdellatif, B., Ebeid, H.M., Shedeed, H.A., Tolba, M.F. (2019). Machine Learning for Enhancement Land Cover and Crop Types Classification. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_4

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