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Multiscale Satellite Image Classification Using Deep Learning Approach

  • Noureldin LabanEmail author
  • Bassam Abdellatif
  • Hala M. Ebied
  • Howida A. Shedeed
  • Mohamed F. Tolba
Chapter
  • 183 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 836)

Abstract

Image classification has been acquiring special importance in the practical applications of remote sensing. This is done with the extraordinary rise of spatial and spectral resolution of satellite imaging sensors. Also it comes from the daily increase of remote sensing databases. Deep learning approaches, especially Convolutional Neural Networks (CNNs) techniques, have been recently outperforming other state-of-the-art classification approaches in various domains. In this chapter, we propose an enhanced technique for classification of satellite images using CNNs. There are two characteristics of satellite images that make performance issue very crucial; first, high information content within the satellite image, and secondly, high computational requisites involved by CNNs. The improvement technique is built on an effective selection of suitable image scale. As this scale achieves a respectively high classification accuracy alongside a minimal computational use. We conduct our proposed technique using three state-of-the-art datasets: WHU-RS Dataset, UCMerced Land Use Dataset, and Brazilian Coffee Scenes Dataset. The proposed technique results in enhancing the accuracy performance, instead of using the original scale directly.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Noureldin Laban
    • 1
    Email author
  • Bassam Abdellatif
    • 1
  • Hala M. Ebied
    • 2
  • Howida A. Shedeed
    • 2
  • Mohamed F. Tolba
    • 2
  1. 1.Data Reception and Analysis DivisionNational Authority for Remote, Sensing and Space ScienceCairoEgypt
  2. 2.Faculty of Computer and Information SciencesAin Shams UniversityCairoEgypt

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