Developing a Generalizable Spectral Classifier for Rhodamine Detection in Aquatic Environments Article Swipe
Ámbar Pérez-García
,
Alba Martín Lorenzo
,
Emma Hernández-Suárez
,
Adrian Rodriguez
,
Tim van Emmerik
,
José F. López
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16163090
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16163090
In environmental studies, rhodamine dyes are commonly used to trace water movements and pollutant dispersion. Remote sensing techniques offer a promising approach to detecting rhodamine and estimating its concentration, enhancing our understanding of water dynamics. However, research is needed to address more complex environments, particularly optically shallow waters, where bottom reflectance can significantly influence the spectral response of the rhodamine. Therefore, this study proposes a novel approach: transferring pre-trained classifiers to develop a generalizable method across different environmental conditions without the need for in situ calibration. Various samples incorporating distilled and seawater on light and dark backgrounds were analyzed. Spectral analysis identified critical detection regions (400–500 nm and 550–650 nm) for estimating rhodamine concentration. Significant spectral variations were observed between light and dark backgrounds, highlighting the necessity for precise background characterization in shallow waters. Enhanced by the Sequential Feature Selector, classification models achieved robust accuracy (>90%) in distinguishing rhodamine concentrations, particularly effective under controlled laboratory conditions. While band transfer was successful (>80%), the transfer of pre-trained models posed a challenge. Strategies such as combining diverse sample sets and applying the first derivative prevent overfitting and improved model generalizability, surpassing 85% accuracy across three of the four scenarios. Therefore, the methodology provides us with a generalizable classifier that can be used across various scenarios without requiring recalibration. Future research aims to expand dataset variability and enhance model applicability across diverse environmental conditions, thereby advancing remote sensing capabilities in water dynamics, environmental monitoring and pollution control.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs16163090
- https://www.mdpi.com/2072-4292/16/16/3090/pdf?version=1724300978
- OA Status
- gold
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401763525
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401763525Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs16163090Digital Object Identifier
- Title
-
Developing a Generalizable Spectral Classifier for Rhodamine Detection in Aquatic EnvironmentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-22Full publication date if available
- Authors
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Ámbar Pérez-García, Alba Martín Lorenzo, Emma Hernández-Suárez, Adrian Rodriguez, Tim van Emmerik, José F. LópezList of authors in order
- Landing page
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https://doi.org/10.3390/rs16163090Publisher landing page
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https://www.mdpi.com/2072-4292/16/16/3090/pdf?version=1724300978Direct link to full text PDF
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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://www.mdpi.com/2072-4292/16/16/3090/pdf?version=1724300978Direct OA link when available
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Computer science, Remote sensing, Environmental science, Biological system, Artificial intelligence, Biology, GeologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 2Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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