Developing a Generalizable Spectral Classifier for Rhodamine Detection in Aquatic Environments

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 16; p. 3090
• Main Authors: Pérez-García, Ámbar, Alba Martín Lorenzo, Hernández, Emma, Rodríguez-Molina, Adrián, Tim H M van Emmerik, López, José F
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Accuracy; Aquatic environment; Artificial intelligence; band selection; Calibration; Cameras; Chemical analysis; Coasts; dye tracking; Environmental conditions; Environmental monitoring; Environmental studies; Methods; Pollution control; Pollution dispersion; Remote control; Remote monitoring; Remote sensing; Rhodamine; Robust control; Seawater; Sensors; Shallow water; Spectral analysis; Spectral sensitivity; Spectrum analysis; Water analysis; Water pollution; Spain
• ISSN: 2072-4292
• DOI: 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.