Orthophotomosaicing Framework for Thermal and Multispectral Images Collected with a UAV for Intelligent Systems
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| Titel: | Orthophotomosaicing Framework for Thermal and Multispectral Images Collected with a UAV for Intelligent Systems |
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| Autoren: | Victor Sineglazov, Kyrylo Lesohorskyi |
| Quelle: | Electronics and Control Systems; Vol. 2 No. 84 (2025); 21-28 Электроника и системы управления; Том 2 № 84 (2025); 21-28 Електроніка та системи управління; Том 2 № 84 (2025); 21-28 |
| Verlagsinformationen: | State University "Kyiv Aviation Institute", 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | machine learning, orthophotomosaicing, mine detection, ортофотоплан, безпілотні літальні апарати, обробка зображень, машинне навчання, unmanned aerial vehicles, image processing, виявлення мін |
| Beschreibung: | 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. |
| Publikationsart: | Article |
| Dateibeschreibung: | application/pdf |
| ISSN: | 1990-5548 |
| DOI: | 10.18372/1990-5548.84.20189 |
| Zugangs-URL: | https://jrnl.nau.edu.ua/index.php/ESU/article/view/20189 |
| Dokumentencode: | edsair.doi.dedup.....0f18bb813446623a58c2784506fea6d8 |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | 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. |
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| ISSN: | 19905548 |
| DOI: | 10.18372/1990-5548.84.20189 |