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Image Semantic Segmentation Based on Convolutional Neural Networks for Monitoring Agricultural Vegetation

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Pattern Recognition and Information Processing (PRIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1055))

Abstract

This paper considers problem of recognition agricultural vegetation state from aerial photographs of various spatial resolutions. Semantic segmentation based on convolutional neural networks is used as a basis for recognition. Two neural networks with SegNet and U-Net architectures are presented and investigated for this aim.

The work was partially supported by Belarusian Republican Foundation for Fundamental Research (project No. Ф18В-005) and the State Committee on Science and Technology of the Republic of Belarus (project no Ф18ПЛШГ-008П).

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Authors and Affiliations

  1. United Institute of Informatics Problems, Surganov st., 6, 220012, Minsk, Belarus

    Valentin Ganchenko & Alexander Doudkin

Authors
  1. Valentin Ganchenko
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  2. Alexander Doudkin
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Corresponding author

Correspondence to Valentin Ganchenko .

Editor information

Editors and Affiliations

  1. Belarusian State University, Minsk, Belarus

    Sergey V. Ablameyko

  2. Belarusian State University, Minsk, Belarus

    Viktor V. Krasnoproshin

  3. Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus

    Maryna M. Lukashevich

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Ganchenko, V., Doudkin, A. (2019). Image Semantic Segmentation Based on Convolutional Neural Networks for Monitoring Agricultural Vegetation. In: Ablameyko, S., Krasnoproshin, V., Lukashevich, M. (eds) Pattern Recognition and Information Processing. PRIP 2019. Communications in Computer and Information Science, vol 1055. Springer, Cham. https://doi.org/10.1007/978-3-030-35430-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-35430-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35429-9

  • Online ISBN: 978-3-030-35430-5

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