Orthophotomosaicing Framework for Thermal and Multispectral Images Collected with a UAV for Intelligent Systems

Authors

DOI:

https://doi.org/10.18372/1990-5548.84.20189

Keywords:

machine learning, orthophotomosaicing, image processing, unmanned aerial vehicles, mine detection

Abstract

In this paper, a framework for orthophotomosaicing of multispectral and thermal images collected by unmanned aerial vehicles is presented. The proposed framework is based on a two-stage data preprocessing and mosaicing orthophotographic restoration of images captured with a route-planned unmanned aerial vehicle collection. The super-resolution and image restoration step is handled via a two-pathway U-net image restoration artificial neural network. The framework simplifies the process and makes the collected data less sensitive to noise via image restoration and upscaling steps. The framework was tested on visible, multispectral and thermal images and provides 3.5% and 5.34% improvements in peak signal-to-noise ratio for multispectral and thermal orthophotomosaics.

Author Biographies

Victor Sineglazov , State University "Kyiv Aviation Institute"

Doctor of Engineering Science

Professor. Head of the Department

Aviation Computer-Integrated Complexes Department

Faculty of Air Navigation Electronics and Telecommunications

Kyrylo Lesohorskyi , National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

PhD Student

Department of Information Systems

Faculty of Informatics and Computer Science

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Published

2025-06-27

Issue

Vol. 2 No. 84 (2025)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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