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Processing Technology of Thematic Identification and Classification of Objects in the Multispectral Remote Sensing Imagery

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Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

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Abstract

This paper is devoted to an automated processing technology for remote sensing data of high spatial resolution. The developed technology is based on an object-based approach, which allows the classification, analysis and identification of individual objects on the Earth’s surface by taking into account their properties. The proposed processing technology includes the following key steps: pre-processing, segmentation, identification of different types of objects, and classification of the whole image. The multiscale segmentation method was used to obtain objects for analysis. The features of an image that allow one to accurately identify different types of objects were calculated: geometric, spectral, spatial, texture, and statistical features. On the basis of the calculated features, a decision on the object class is made. A model based on fuzzy inference is chosen to decide on the classes of image segments. The general accuracy, which shows the percentage of correctly classified pixels, and the Kappa index were used to evaluate the classification results.

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Authors and Affiliations

  1. Dnipro University of Technology, 19 Dmytra Yavornytskoho Ave., Dnipro, 49005, Ukraine

    Volodymyr Hnatushenko, Yana Shedlovska & Igor Shedlovsky

Authors
  1. Volodymyr Hnatushenko
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  2. Yana Shedlovska
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  3. Igor Shedlovsky
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Corresponding author

Correspondence to Yana Shedlovska .

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Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Hnatushenko, V., Shedlovska, Y., Shedlovsky, I. (2023). Processing Technology of Thematic Identification and Classification of Objects in the Multispectral Remote Sensing Imagery. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_24

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