Water pollution classification and detection by hyperspectral imaging

• DOI: 10.1364/oe.522932
• Publication Date: 2024-06-03T16:00:12Z
• Authors: Joseph-Hang Leung; Yu-Ming Tsao; Riya Karmakar; Arvind Mukundan; Song-Cun Lu; Shuan-Yu Huang; Penchun Saenprasarn; Chi-Hung Lo; Hsiang-Chen Wang
• Keywords: RGB color model

This study utilizes spectral analysis to quantify water pollutants by analyzing the images of biological oxygen demand (BOD). In this study, a total 2545 depicting quality pollution were generated due absence standardized detection method. A novel snap-shot hyperspectral imaging (HSI) conversion algorithm has been developed conduct on traditional RGB images. order demonstrate effectiveness HSI algorithm, two distinct three-dimensional convolution neural networks (3D-CNN) are employed train separate datasets. One dataset is based (HSI-3DCNN), while other (RGB-3DCNN). The categorized into three groups: Good, Normal, and Severe, extent severity. comparison was conducted between models, focusing precision, recall, F1-score, accuracy. model's accuracy improved from 76% 80% when RGB-3DCNN substituted with HSI-3DCNN. results suggest that capacity enhance compared model.