Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering


  • Ehsan Ali Al-Zubaidi Department of Environmental Planning, Faculty of Physical Planning, University of Kufa, Najaf, Iraq
  • Riyad Ghafel Abdul Hussein Al Khafaji Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Iraq




Clustering, Unsupervised Learning, Satellite Image, Landsat-8 TM


Remote sensing, specifically satellite imagery, is gaining prominence in computer science nowadays, in the era of artificial intelligence, in an attempt to deliver more precise information. The satellite images of Earth are gathered, evaluated, and processed for use in civil and military applications with a military aim. Satellite images do have a wide range of services. The areas of study of agriculture, fishery, oceanography, and meteorology include geology, biodiversity, cartography, land use planning, and armed conflict. Transformation is the goal of the categorization of satellite images. Transformation of satellite images into information that can be used rather than having an image of a location. This paper classified a scene of the Landsat-8 satellites with specifications (Path=168 and Row=38). This scene was classified into four categories (Water, Vegetation, bare land, and Build-up) based on the unsupervised classification method (ISO Clustering). The ISO Clustering method is found in the Arc Map program. The results regarding classification accuracy are a good percentage compared to unsupervised Classification.


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How to Cite

E. A. Al-Zubaidi and R. Al Khafaji, “Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering”, WJCMS, vol. 3, no. 1, pp. 40–53, Mar. 2024, doi: 10.31185/wjcms.212.