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

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.

Authors

Institutions

Publication Details

Journal
Electronics and Control Systems
Published
2025-06-27
DOI
https://doi.org/10.18372/1990-5548.84.20189
Primary Topic
Infrared Target Detection Methodologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Kyrylo Lesohorskyi, Victor Sineglazov
Electronics and Control Systems
Infrared Target Detection Methodologies
article

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

Kyrylo Lesohorskyi, Victor Sineglazov
article en

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.

Electronics and Control SystemsVol. 2(84)
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (UA), National University "Kyiv Aviation Institute" (UA)
Openalex Percentile: Top 76%
Infrared Target Detection Methodologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.