A Scale-Invariant Feature Description and Identification Method for Organic Pollutants in Urban River With Background Fluctuation Using 3-D Fluorescence Spectroscopy

Detection and identification of organic pollutants in urban rivers are essential for environmental protection and drinking water safety. As an online detection technology, 3-D fluorescence spectroscopy provides rich material information and has no secondary pollution. However, existing approaches still face low recognition rates caused by background fluctuations in water quality, especially for pollutants in low concentrations or with similar spectra. In this study, a recognition method, feature descriptor-Fisher vector (FD-FV), which combines the chemical properties of pollutants with the fluorescence detail features of the water background, is proposed to solve the above problems. Pollutant descriptor coupled with fluorescence peaks of the spectral image is designed to obtain feature vectors containing pollutant scale, location, and orientation from excitation-emission matrix (EEM) data. Statistical properties of pollutants are obtained by using Fisher vector coding including background fluorescence in river water. Then, the cosine distance method is used to determine the pollutant category. Eleven organic pollutants were gathered and examined to obtain a sample library of distinctive organic pollutants in river waters. To confirm the method’s efficacy, an online simulation of river water was built. The experimental results indicate that the method achieved overall accuracies of 93%, 86%, and 91.16% under three challenging conditions: fluctuations in background water quality, low pollutant concentrations, and the identification of out-of-library pollutants. Notably, the present method’s accuracy exceeds that of several conventional benchmark methods.