Remotely sensed hyperspectral data can support more effective water quality monitoring. Nevertheless, the variability and complexity of urban river water make it hard to retrieve comprehensive water quality characteristics directly, so that most current water quality assessments rely on semiempirical, semianalytical, or bio-optical approaches. In this study, we carried out simultaneous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ hyperspectral data and water quality measurements. We used the 382 hyperspectral data from urban rivers of Zhongshan City in the Pearl River Delta to test how well the random forest (RF) and one-dimensional convolutional neural networks (1D-CNNs) algorithms retrieved the newly established water quality index (WQI). The RF algorithm also identified essential wavelengths for retrieving the WQI. Our results demonstrate that the RF and 1D-CNN algorithms performed well in WQI estimations. The 1D-CNN model performed significantly better than the RF model, especially on high WQI samples. Both models were insensitive to smoothing of the hyperspectral data, showing that the noise of the original hyperspectral reflectance data has a limited impact on the algorithms. In addition, when we used the essential wavelength data (mainly located between 580–590 nm and near 722, 751, 821, and 830 nm) as input data, we achieved better retrieval results. The 1D-CNN model performed the best with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.87, RMSE of 0.574, and RPIQ of 3.082 when we used the top tenth percentile of the essential wavelength data. This study demonstrates the potential of the 1D-CNN algorithm for hyperspectral data analysis to retrieve comprehensive water quality.

Using Machine Learning Algorithms With In Situ Hyperspectral Reflectance Data to Assess Comprehensive Water Quality of Urban Rivers

Remotely sensed hyperspectral data can support more effective water quality monitoring. Nevertheless, the variability and complexity of urban river water make it hard to retrieve comprehensive water quality characteristics directly, so that most current water quality assessments rely on semi-empirical, semi-analytical or bio-optical approaches. In this study, we carried out simultaneous in-situ hyperspectral data and water quality measurements. We used the 382 hyperspectral data from urban rivers of Zhongshan City in the Pearl River Delta to test how well the Random Forest (RF) and One-dimensional Convolutional Neural Networks (1D-CNN) algorithms retrieved the newly established water quality index (WQI). The RF algorithm also identified essential wavelengths for retrieving the WQI. Our results demonstrate that the RF and 1D-CNN algorithms performed well in WQI estimations. The 1D-CNN model performed significantly better than the RF model, especially on high WQI samples. Both models were insensitive to smoothing of the hyperspectral data, showing that the noise of the original hyperspectral reflectance data has a limited impact on the algorithms. Additionally, when we used the essential wavelength data (mainly located between 580 nm to 590 nm and near 722 nm, 751nm, 821 nm, 830 nm) as input data, we achieved better retrieval results. The 1D-CNN model performed the best with an R2 of 0.87, RMSE of 0.574, and RPIQ of 3.082 when we used the top 10th percentile of the essential wavelength data. This study demonstrates the potential of the 1D-CNN algorithm for hyperspectral data analysis to retrieve comprehensive water quality.

Using machine learning algorithms with in-situ hyperspectral reflectance data to assess comprehensive water quality of urban rivers

Unmanned aerial vehicle (UAV) hyperspectral imaging has been extensively applied in various fields. However, due to the limited imaging width, hyperspectral images (HSIs) captured by UAV need to be stitched, so as to effectively cover the study area. In this article, an effective seamless stitching method with deep feature matching and elastic warp is proposed for HSIs, which consists of the following major steps. First, for each input HSI, a single-band gray-scale image is obtained by fusing the bands corresponding to the red, green, and blue wavelengths. Second, the feature points of each HSI are obtained with a robust VGG-style network and matched with a graph neural network. After point pairs are obtained, the next step is to estimate the transformation matrix of adjacent images, and a spectral correction method based on intrinsic decomposition is proposed to ensure the spectral consistency of adjacent images. In the final stage, a seam-cutting and multiscale blending strategy is adopted to ensure the spatial consistency of the stitching results. Experimental results on real HSIs show that the proposed method is superior to six representative image stitching approaches. 

A Robust UAV Hyperspectral Image Stitching Method Based on Deep Feature Matching

This article reports the development of an underwater imaging system and its trial on a moored surface buoy for in situ plankton monitoring of coastal waters. The imager features shadowless white light illumination by an orthogonal lamellar lighting design, resulting in high-quality underwater darkfield color imaging of planktonic particles in the size range of &#x223C;200 &#x03BC;m to 40 mm and effective reduction of zooplankton phototaxis. Through raft and buoy trials, 46&#x2009;804 plankton and suspending particle images have been annotated through a human&#x2013;machine mutually assisted effort into a data set with 90 categories. In the meanwhile, a deep learning model based on a triclassification VGGNet-11 and multiclassification ResNet-18 convolutional neuron networks in a two-staged hierarchy has also been trained and developed. The model has been applied with human supervision to semiautomatically analyze a total of 1&#x2009;545&#x2009;187 images obtained from a buoy trial for six months from late spring to early winter of 2020. The high temporal resolution results well documented the variation of the mesoplankton community structure in two time series of 38 days in summer and 54 days in autumn of the target sea region. In addition, the dominant species in the trial period and a zooplankton outbreak that had threatened the safety of the nearby nuclear power plants were quantitatively analyzed. The system is expected to become a new entry into the toolkit for marine surface buoy platforms to upscale their capabilities for more comprehensive in situ plankton monitoring.

Development of a Buoy-Borne Underwater Imaging System for In Situ Mesoplankton Monitoring of Coastal Waters

Thermal infrared (TIR) technology is crucial for wildlife detection in unmanned aerial vehicles (UAVs), allowing executives to explore and detect at night. However, the images captured by TIR cameras are unavoidably affected by various unexpected challenges such as image jitter, wildlife overlap, and fog, which may drastically decrease wildlife detection ability. To overcome these challenges, we propose a high-accuracy infrared object detection method called channel enhancement RetinaNet (CE-RetinaNet). Firstly, a new channel enhancement (CE) module is proposed to strengthen the feature extraction of infrared images. Then, a new batch-norm stochastic channel attention (BSCA) module is proposed to filter occlusion-caused anomalous activations and focus on the pixel in the same position across channels. Next, a path augmentation (PA) operation is added after the feature pyramid network (FPN) to improve the localization capability at the entire feature level. Finally, we modified the output strategy of the classification and regression subnets. Additionally, we built a TIR wildlife detection dataset called the Infrared Salient Object Detection (ISOD) comprising 2534 images, which is accessible by the website: https://doi.org/10.5281/zenodo.7445307. We conduct extensive experiments on both public and ISOD datasets, and the experimental results reveal that CE-RetinaNet obtains higher average precision (AP) (e.g., 11.3% more) and Recall (e.g., 11.6% more) compared to other state-of-the-art object detectors. 

CE-RetinaNet: A Channel Enhancement Method for Infrared Wildlife Detection in UAV Images

Phaeocystis globosa (P. globosa) is a unique causative species of harmful algal blooms, which can form gelatinous colonies. We, for the first time, used unmanned aerial vehicle (UAV) measurements to identify P. globosa blooms and to quantify the biomass. Based on in situ measured remote sensing reflectance (Rrs), it is found that, for P. globosa blooms, the maximum of the second-derivative ( dλ²Rrs) of Rrs(λ) in the 460-480-nm domain is beyond 466 nm. An analysis of the absorption properties from algal cultures suggested that this feature comes from the absorption of chlorophyll c₃ (Chl-/ c₃) around 466 nm, a prominent feature of P. globosa. This position of dλ²Rrs maximum was, thus, selected as the criterion for P. globosa identification. The spatial extent of P. globosa blooms in two bays off southern China was then mapped by applying the criterion to UAV-measured Rrs. Twelve out of 16 UAV and in situ match-up stations were consistently identified as dominated by P. globosa, indicating the accuracy of 75%. Furthermore, using localized empirical models, chlorophyll a (Chl-/ a) concentration and colony numbers of P. globosa were estimated from UAV-derived Rrs, where P. globosa colonies were found in a range of ~3-37 gel matrix/L, indicating the occurrence of weak to moderate P. globosa blooms during the surveys. The promising results suggest a high potential for detection and quantification of P. globosa blooms in near-shore bays or harbors using UAV-based hyperspectral remote sensing, where conventional ocean color satellite remote sensing runs into difficulties.

Detection and Biomass Estimation of Phaeocystis globosa Blooms off Southern China From UAV-Based Hyperspectral Measurements

Effects of environmental factors on mycoplankton diversity and trophic modes in coastal surface water

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Detection and Biomass Estimation of Phaeocystis globosa Blooms off Southern China From UAV-Based Hyperspectral Measurements

Effects of environmental factors on mycoplankton diversity and trophic modes in coastal surface water