DEEP INFORMATION-EXTREME MACHINE LEARNING FOR AUTONOMOUS UAV BASED ON DECURSIVE DATA STRUCTURE - SEMANTIC SEGMENTATION OF DIGITAL IMAGE OF A REGION

Aug 3, 2026

The research presents functional categorical models for deep information-extreme machine learning using linear and hierarchical data structures and parameter optimization guided by information criteria. A decursive binary data tree is constructed for a given alphabet of recognition classes to improve onboard semantic segmentation of region images from an optoelectronic observation channel on an autonomous UAV. The method maximizes average interclass Hamming-code distance and sets deferred-decision depth by optimizing system parameters. A modified Kullback-Leibler measure optimizes learning parameters and transforms the input training matrix into a working binary Hamming-space matrix that adapts to maximum accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

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Introduction

Motivation

State of the Art

UDC 004.93.1 doi: 10.32620/reks.2025.3.09 Valerii CHERANOVSKYI1, Mykyta МYRONENKO2, Serhii KOVALEVSKYI2, Roman KRASKOVSKYI2, Mykhailo OTROSHCHENKO2 1 Scientific Research Institute “Problems of Physical Modeling of Aircraft Modes”, composed of National Aerospace University «Kharkiv Aviation Institute», Kharkiv, Ukraine 2 Sumy State University, Sumy, Ukraine DEEP INFORMATION-EXTREME MACHINE LEARNING FOR AUTONOMOUS UAV BASED ON DECURSIVE DATA STRUCTURE FOR SEMANTIC SEGMENTATION OF DIGITAL IMAGE OF A REGION The subject of the research is functional categorical models of deep information-extreme machine learning based on linear and hierarchical data structures, methods for optimizing machine learning parameters based on information criteria and constructing a decursive binary data tree for a given alphabet of recognition classes. The aim of the research is to improve the accuracy of machine learning for an autonomous UAV for semantic segmentation of a digital image of a region obtained via an optoelectronic observation channel. This goal is achieved by developing a method of deep information-extreme machine learning for an on-board recognition system of an autonomous UAV using a decursive binary data structure. A new method of deep information-extreme machine learning for autonomous UAVs has been developed, based on a hierarchical data structure in the form of a decursive binary tree. The novelty of the method lies in the maximization of the average interclass code distance within a given dimensionality of the Hamming feature space by optimizing the selection level of coordinates of statistically averaged binary realizations of the recognition classes. At the same time, the level of depth of information-extreme machine learning according to the principle of deferred decisions is determined by the number of parameters of the system's functioning that are optimized according to the information criterion. This approach, unlike neural-like structures, provides flexibility for the onboard recognition system during retraining in the event of an expansion of the recognition class alphabet. The Kullback-Leibler information measure modified by the authors serves as a criterion for optimizing machine learning parameters. In addition, the proposed method involves the transformation of the input training matrix into a working binary matrix specified in the Hamming space, which in the process of machine learning adapts to its maximum accuracy. Results: Based on the results of deep information-extreme machine learning, errorfree decision rules based on the training matrix were constructed within the framework of a geometric approach. It is shown that the accuracy of the deep information-extreme machine learning is affected by the sequence of optimization of the parameters of the recognition system. The results of functional testing and cross-validation have confirmed the high accuracy of information-extreme machine learning for an autonomous UAV, as demonstrated by semantic segmentation of a digital image of a region. Conclusions: For the first time, a method of deep information-extreme machine learning based on a hierarchical data structure in the form of a decursive binary tree has been developed, which, unlike the known ones, additionally optimizes the level of selection for coordinates of binary averaged vectors of recognition features. Keywords: information-extreme machine learning; information criterion; optimization; autonomous UAV; decursive binary tree; digital image of the region. 1. Introduction 1.1. Motivation Providing a UAV with autonomy allows expanding its functionality, increasing the probability of mission completion, and reducing the burden on the personnel of the ground control station. There are known examples of the use of autonomous UAVs in the agricultural sector [1, 2], for monitoring nuclear power plants [3], and ecosystems [4, 5], recognition of ground objects [6, 7], military [8], etc. One of the a

FAQs

What problem does the paper address for autonomous UAVs?

It targets the development of a highly accurate and operational onboard machine learning method for semantic segmentation of a digital image of an observation region captured through an optoelectronic channel.

What is the core contribution of the proposed method?

It introduces deep information-extreme machine learning based on a hierarchical decursive binary tree structure, optimizing coordinate selection in a Hamming feature space to maximize interclass code distance.

How are model parameters optimized in the approach?

The paper uses a modified Kullback-Leibler information measure as the information criterion to optimize the functioning parameters and determine the deferred-decisions depth.

Aug 3, 2026

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