Кафедра інформаційних технологій і систем (ДМетІ)
Permanent URI for this community
UK: Кафедра інформаційних технологій і систем (Дніпровський металургійний інститут, ДМетІ)
EN: Department of Information Technologies and Systems (DMetI)
Browse
Browsing Кафедра інформаційних технологій і систем (ДМетІ) by Author "Gorev, Vyacheslav"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
Item Identification of Objects on Satellite Images Using the Image Texture Properties(CEUR-WS Team, Aachen, Germany, 2023) Hnatushenko, Volodymyr V.; Shedlovska, Yana; Shedlovsky, Igor; Gorev, VyacheslavENG: This paper focuses on identifying objects in satellite images using image texture properties, which is an important problem in agriculture. Texture segmentation can distinguish areas that correspond to tree plantations. Orchards and tree plantations can cover vast areas with thousands of trees, making the automation of harvest estimation crucial. Satellite images enable the creation of an effective automatic system for counting trees in plantations. In this work, we applied image texture segmentation to identify areas corresponding to agricultural plantations. We calculated textural properties of the image using the gray-level cooccurrence matrix, including mean value, variance, homogeneity, second angular moment, correlation, contrast, divergence, and entropy. These characteristics were used for segmentation, with multi-scale segmentation employed to distinguish areas of the image with specific textures. We proposed an algorithm for counting objects in satellite images, based on identifying individual objects that create a texture according to their spectral characteristics. The images used in this work primarily featured three object classes: trees, soil, and tree shadows. Since trees in gardens and plantations are arranged uniformly and have the same size, they can be easily distinguished from other image pixels based on their spectral characteristics. We analyzed NDVI and NSVDI spectral indices for tree detection and used the automatic spectral index histogram splitting method to distinguish objects with a high index value corresponding to trees.