SEGMENTATION OF AEROSPACE IMAGES BY A NON-STANDARD APPROACH USING INFORMATIVE TEXTURAL FEATURES
Yerzhanova A. Abdikerimova G. Alimova Z. Slanbekova A. Tungatarova A. Muratkhan R. Borankulova G. Zhunussova G.
2022Technology Center
Eastern-European Journal of Enterprise Technologies
2022#1Issue 2-11539 - 49 pp.
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
Agricultural crops , Clustering , Image processing , Ndvi , Pests , Satellite images , Sbc , Textural features , Weeds
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Department of Information Systems, L. N. Gumilyov Eurasian National University, Satpayev str.,2, Nur-Sultan, 010008, Kazakhstan
S. Seifullin Kazakh Agrotechnical University, Pobeda ave.,62, Nur-Sultan, 010000, Kazakhstan
Toraighyrov University, Lomova str.,64, Pavlodar, 140008, Kazakhstan
Department of Applied Mathematics and Computer Science, Karaganda Buketov University, Universitetskaya str.,28, Karaganda, 100024, Kazakhstan
Department of Information Systems, M. Kh. Dulaty Taraz Regional University, Tole bi str.,40, Taraz, 080000, Kazakhstan
Department of Informatics And Biostatistics, Karaganda Medical University, Gogol str.,40, Karaganda, Kazakhstan
Department of Information Systems
S. Seifullin Kazakh Agrotechnical University
Toraighyrov University
Department of Applied Mathematics and Computer Science
Department of Information Systems
Department of Informatics And Biostatistics
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