Hierarchical Information-Extreme Machine Learning of Uav for Semantic Segmentation for a Digital Image of the Region Using a Decursive Data Structure

The purpose of the study is to increase the accuracy of machine learning of an autonomous unmanned aerial vehicle (UAV) for identifying frames of a digital image of the observation region. A functional categorical model is proposed, on the basis of which an algorithm for information-extreme machine learning of an autonomous UAV by linear data structure with optimization of control tolerances for recognition features is developed and programmatically implemented. The formation of the input training brightness matrix was carried out by using the Cartesian coordinate system to process the brightness values for digital images of machine learning objects that belonged to the “texture” type. The modified Kullback measure was used as a criterion for optimizing machine learning parameters. Since the implementation of machine learning on a linear data structure did not allow to achieve high accuracy of machine learning, information-extreme machine learning was implemented on a hierarchical structure in the form of a decursive binary tree. The transition from a linear data structure to a hierarchical one allowed to reduce multi-class machine learning to the two-class learning at each stratum of a decursive binary tree, which allowed to increase the averaged value of the information criterion over the strata of the decursive tree. For the recognition classes in the stratum of decursive tree, where high accuracy of machine learning was not obtained, information-extreme machine learning was implemented with sequential optimization of parameters. As a result, it was possible to construct decision rules that are error-free according to the training matrix. In addition, it was experimentally proven that when the number of recognition classes is more than two, it is advisable to switch to information-extreme machine learning on a hierarchical data structure in the form of a decursive binary tree.