UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring

Abstract

Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV air–ground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 μg/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water.

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

“…Despite that most of the aerial remote sensing techniques in these contexts are based on satellite or airborne systems, mainly due to their wide coverage, UAVs open up a new set of significant opportunities to overcome the still too-limited imagery resolution, as well as the coarse and often discontinuous acquisition rate. By exploiting the improved spatial and temporal resolution and the availability of multiple sensors, UAVs can be used to carry out in-depth water quality analyses based on multiple correlated parameters [183]. More specifically, peculiar characteristics of the water surface reflectance captured by hyperspectral cameras can be used to detect suspended solids [184], particulate matter [185], and even toxic agents (chemical, biological) [186], while machine learning tools (e.g., SVM) turned out to be very powerful solutions to identify the presence of oil spills in optical images [187,188].…”

Review of acousto-optic spectral systems and applications

“…In addition, acousto-optic spectroscopy systems are also widely used in agriculture, forestry, pharmaceutical analysis, and environmental monitoring [79]. Hong Liu et al designed a drone-based hyperspectral imaging remote sensing system in 2021 [80], which can be used for activities such as water surface remote sensing, imaging, and spectral analysis. In agriculture, a commercial AOTF-based near-infrared spectrometer is already available for the non-destructive detection of agricultural products such as dried apples and olive fruit [81].…”

Useful Feature Extraction and Machine Learning Techniques for Identifying Unique Pattern Signatures Present in Hyperspectral Image Data

“…Associated AI and machine learning applications are the mainstay of these informational systems. In water quality analysis, various hyperspectral algorithms such as partial least-squares, fully connected neural networks with backpropagation (FCNN-BP), Support vector machine (SVM) and Random Forest (RF) procedures have been successfully used and compared for quantitative investigation [15]. Other assessments have been implemented using FETs and AI for detecting water contamination in rivers [16], forest fire assessment, and automated drone team hyperspectral fusion.…”