Computer Vision Corrections Enhance UAV-Based Retrievals in Shallow Waters Article Swipe
Dario Scilla
,
Omar A. Lopez
,
Brian Owain Nieuwenhuis
,
Kasper Johansen
,
Mariana Elías-Lara
,
Víctor Angulo
,
Jorge Rodríguez
,
Burton H. Jones
,
Matthew F. McCabe
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3587478
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3587478
Traditional shallow water (<5 m) survey methods are costly and limited in coverage due to logistical challenges in deploying imaging sensors in remote locations. Such constraints limit large-scale monitoring of near-shore benthic environments, which are vital for biodiversity and coastal ecosystem health. Unmanned aerial vehicles (UAVs) offer a cost-effective alternative for high-resolution data collection over broader areas. However, common air–water interface phenomena, such as light refraction, caustics, and sun glint, significantly impact data quality and interpretability. In this study, we explore the use of color transferring techniques and image averaging approaches to mitigate the optical distortions caused by refraction and sun glint. We demonstrate the utility of our approach using UAV-based full-HD 60 frames per second videos captured at varying altitudes, ranging from 10 to 120 m height, over a shallow water lagoon that features a mix of coral reefs, rubble, and sandy substrates. By applying color transferring algorithms and median filtering, we achieved greater than 10% improvement in the correlation between frames at low altitudes (<20 m height), where the refraction phenomenon is more dominant, while also restoring geometries with errors of less than 5% compared with the true shape dimensions. The best results were achieved using 60 video frames. Comparison against the existing methods highlighted the efficacy of our approach in enhancing data quality and reducing refraction, caustics, and sun glint phenomena, enabling more precise object delineation and habitat assessments. Our method significantly improves image clarity and interpretability, supporting ecological studies and conservation in shallow water ecosystems.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2025.3587478
- OA Status
- gold
- Cited By
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All OpenAlex metadata
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https://doi.org/10.1109/jstars.2025.3587478Digital Object Identifier
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Computer Vision Corrections Enhance UAV-Based Retrievals in Shallow WatersWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Dario Scilla, Omar A. Lopez, Brian Owain Nieuwenhuis, Kasper Johansen, Mariana Elías-Lara, Víctor Angulo, Jorge Rodríguez, Burton H. Jones, Matthew F. McCabeList of authors in order
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/jstars.2025.3587478Direct OA link when available
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