Segmentation of aerospace images by a non-standard approach using informative textural features
DOI:
https://doi.org/10.15587/1729-4061.2022.253188Keywords:
image processing, satellite images, textural features, SBC, NDVI, clustering, agricultural crops, weeds, pestsAbstract
The article presents an analysis of a non-standard approach to the segmentation of textural areas in aerospace images. The question of the applicability of sets of textural features for the analysis of experimental data is being investigated to identify characteristic areas on aerospace images that in the future it will be possible to identify types of crops, weeds, diseases, and pests. The selection of suitable algorithms was carried out and appropriate software tools were created on Matlab 2021a and in the software package for statistical analysis Statistica 12.
The main way to extract information is to decrypt images, which are the main carrier of information about the underlying surface. The main tasks of texture area analysis include selection and formation of features describing textural differences; selection and segmentation of textural areas; classification of textural areas; identification of an object by texture.
To solve the tasks, spectral brightness coefficient (SBC), Normalized Difference Vegetation Index (NDVI), textural features of various crops and weeds. Much attention will be paid to the development of software tools that allow the selection of features describing textural differences for the segmentation of textural areas into subdomains. That is the question of the applicability of sets of textural features and other parameters for the analysis of experimental data to identify types of soils and soils, vegetation types, humidity, crop damage in aerospace images will be resolved.
This approach is universal and has great potential for identifying objects using image clustering. To identify the boundaries of areas with different properties of the image under study, images of the same surface area taken at different times are considered.
Supporting Agency
- For providing data on agricultural crops of Northern Kazakhstan in the preparation of this article, the author expresses gratitude to the Scientific and Production Center of Grain Farming named after A.I. Barayev.
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Copyright (c) 2022 Akbota Yerzhanova, Gulzira Abdikerimova, Zhanar Alimova, Assylzat Slanbekova, Aigul Tungatarova, Raikhan Muratkhan, Gaukhar Borankulova, Gulzat Zhunussova
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