ALGORITHMS FOR FEATURE EXTRACTION AND OPTIMISATION OF OBJECT RECOGNITION OPERATOR
Annotatsiya
This paper deals with the development and analysis of feature extraction and optimisation algorithms for object recognition operators. Different algorithms are used to improve the efficiency of recognition operators in the automatic analysis of remote sensing images. The information model of objects and methods for selecting, extracting and optimising their features have been studied. The issue of extracting important features of objects using spectral, textural and statistical features and constructing optimal operators based on them has also been studied. Effective approaches based on the theory of convex hulls and multidimensional analysis methods have been proposed, taking into account the mutual compensation properties of operators. The proposed algorithms aim at increasing the accuracy of object classification and are used to improve the process of object recognition in remote sensing images. The results can be used in artificial intelligence and machine learning technologies and have broad applications in agriculture, ecology, urban planning and natural resource monitoring.