Retrieving Water Quality Parameters from Noisy-Label Data Based on Instance Selection

As an important part of the "air–ground" integrated water quality monitoring system, the inversion of water quality from unmanned airborne hyperspectral image has attracted more and more attention. Meanwhile, unmanned aerial vehicles (UAVs) have the characteristics of small size, flexibility and quick response, and can complete the task of water environment detection in a large area, thus avoiding the difficulty in obtaining satellite data and the limitation of single-point monitoring by ground stations. Most researchers use UAV for water quality monitoring, they take water samples back to library or directly use portable sensors for measurement while flying drones at the same time. Due to the UAV speed and route planning, the actual sampling time and the UAV passing time cannot be guaranteed to be completely synchronized, and there will be a difference of a few minutes. For water quality parameters such as chromaticity (chroma), chlorophyll-a (chl-a), chemical oxygen demand (COD), etc., the changes in a few minutes are small and negligible. However, for the turbidity, especially in flowing water body, this value of it will change within a certain range. This phenomenon will lead to noise error in the measured suspended matter or turbidity, which will affect the performance of regression model and retrieval accuracy. In this study, to solve the quality problem of label data in a flowing water body, an unmanned airborne hyperspectral water quality retrieval experiment was carried out in the Xiao River in Xi’an, China, which verified the rationality and effectiveness of label denoising analysis of different water quality parameters. To identify noisy label instances efficiently, we proposed an instance selection scheme. Furthermore, considering the limitation of the dataset samples and the characteristic of regression task, we build a 1DCNN model combining a self attention mechanism (SAM) and the network achieves the best retrieving performance on turbidity and chroma data. The experiment results show that, for flowing water body, the noisy-label instance selection method can improve retrieval performance slightly on the COD parameter, but improve greatly on turbidity and chroma data.