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19 pages, 8393 KiB

Open AccessArticle

A Machine-Learning-Based Framework for Retrieving Water Quality Parameters in Urban Rivers Using UAV Hyperspectral Images

by Bing Liu and Tianhong Li

Remote Sens. 2024, 16(5), 905; https://doi.org/10.3390/rs16050905 - 04 Mar 2024

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Abstract

Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking [...] Read more.

Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (COD_Mn). The highest R²s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the COD_Mn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers. Full article

(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing)

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27 pages, 12989 KiB

Open AccessArticle

A FEM Flow Impact Acoustic Model Applied to Rapid Computation of Ocean-Acoustic Remote Sensing in Mesoscale Eddy Seas

by Yi Liu, Jian Xu, Kangkang Jin, Rui Feng, Luochuan Xu, Linglong Chen, Dan Chen and Jiyao Qiao

Remote Sens. 2024, 16(2), 326; https://doi.org/10.3390/rs16020326 - 12 Jan 2024

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Abstract

Mesoscale eddies have an impact on the marine environment mainly in two areas, namely, currents and changes in the sound velocity gradient due to temperature and salt stirring. The traditional underwater-related remote sensing acoustic remote sensing model is capable of analyzing the acoustic [...] Read more.

Mesoscale eddies have an impact on the marine environment mainly in two areas, namely, currents and changes in the sound velocity gradient due to temperature and salt stirring. The traditional underwater-related remote sensing acoustic remote sensing model is capable of analyzing the acoustic field under the change in sound velocity gradient, but it is not capable of analyzing the acoustic field under the influence of ocean currents. In order to more effectively analyze the changes in the acoustic field caused by mesoscale eddies, this paper proposes a FEM flow impact model applied to the rapid computation of acoustic remote sensing of mesoscale eddies in the sea area. The algorithm first performs a grid optimization of the sea area model based on vertical sound velocity variations and completes the classification of sound velocity layer junctions. At the same time, we construct the sound velocity gradient environment affected by the mesoscale eddy and then simplify the fluid flow in the mesoscale eddy into a non-viscous and non-rotating velocity potential, and then participate in the solution of the three-dimensional spatial fluctuation equations in the form of time-harmonic in the frequency domain, from which we can obtain the truncated sound pressure as well as the propagation loss, and quickly and completely characterize the acoustic remote sensing of the sea area of the mesoscale eddy. The paper verifies the effectiveness of the algorithm through SW06-contained flow experiments and further proposes an optimization formula for sound velocity inversion. We analyze this using measured data of mesoscale eddy fields in the Bering Sea waters during 2022 and find that eddies have a greater effect on the propagation of the acoustic field along their flow direction. Full article

(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing)

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22 pages, 9907 KiB

Open AccessArticle

An Automatic Deep Learning Bowhead Whale Whistle Recognizing Method Based on Adaptive SWT: Applying to the Beaufort Sea

by Rui Feng, Jian Xu, Kangkang Jin, Luochuan Xu, Yi Liu, Dan Chen and Linglong Chen

Remote Sens. 2023, 15(22), 5346; https://doi.org/10.3390/rs15225346 - 13 Nov 2023

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Abstract

The bowhead whale is a vital component of the maritime environment. Using deep learning techniques to recognize bowhead whales accurately and efficiently is crucial for their protection. Marine acoustic remote sensing technology is currently an important method to recognize bowhead whales. Adaptive SWT [...] Read more.

The bowhead whale is a vital component of the maritime environment. Using deep learning techniques to recognize bowhead whales accurately and efficiently is crucial for their protection. Marine acoustic remote sensing technology is currently an important method to recognize bowhead whales. Adaptive SWT is used to extract the acoustic features of bowhead whales. The CNN-LSTM deep learning model was constructed to recognize bowhead whale voices. Compared to STFT, the adaptive SWT used in this study raises the SCR for the stationary and nonstationary bowhead whale whistles by 88.20% and 92.05%, respectively. Ten-fold cross-validation yields an average recognition accuracy of 92.85%. The method efficiency of this work was further confirmed by the consistency found in the Beaufort Sea recognition results and the fisheries ecological study. The research results in this paper help promote the application of marine acoustic remote sensing technology and the conservation of bowhead whales. Full article

(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing)

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17 pages, 8022 KiB

Open AccessArticle

Underwater Image Restoration via Adaptive Color Correction and Contrast Enhancement Fusion

by Weihong Zhang, Xiaobo Li, Shuping Xu, Xujin Li, Yiguang Yang, Degang Xu, Tiegen Liu and Haofeng Hu

Remote Sens. 2023, 15(19), 4699; https://doi.org/10.3390/rs15194699 - 25 Sep 2023

Cited by 4 | Viewed by 1515

Abstract

When light traverses through water, it undergoes influence from the absorption and scattering of particles, resulting in diminished contrast and color distortion within underwater imaging. These effects further constrain the observation of underwater environments and the extraction of features from submerged objects. To [...] Read more.

When light traverses through water, it undergoes influence from the absorption and scattering of particles, resulting in diminished contrast and color distortion within underwater imaging. These effects further constrain the observation of underwater environments and the extraction of features from submerged objects. To address these challenges, we introduce an underwater color image processing approach, which amalgamates the frequency and spatial domains, enhancing image contrast in the frequency domain, adaptively refining image color within the spatial domain, and ultimately merging the contrast-enhanced image with the color-corrected counterpart within the CIE L*a*b* color space. Experiments conducted on standard underwater image benchmark datasets highlight the significant improvements our proposed method achieves in terms of enhancing contrast and rendering more natural colors compared to several state-of-the-art methods. The results are further evaluated using four commonly used image metrics, consistently showing that our method yields the highest average value. The proposed method effectively addresses challenges related to low contrast, color distortion, and obscured details in underwater images, a fact especially evident in various scenarios involving color-affected underwater imagery. Full article

(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing)

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13 pages, 2268 KiB

Open AccessCommunication

A High-Performance Thin-Film Sensor in 6G for Remote Sensing of the Sea Surface

by Qi Song, Xiaoguang Xu, Jianchen Zi, Jiatong Wang, Zhongze Peng, Bingyuan Zhang and Min Zhang

Remote Sens. 2023, 15(14), 3682; https://doi.org/10.3390/rs15143682 - 24 Jul 2023

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Abstract

Functional devices in the THz band will provide a highly important technical guarantee for the promotion and application of 6G technology. We sought to design a high-performance sensor with a large area, high responsiveness, and low equivalent noise power, which is stable at [...] Read more.

Functional devices in the THz band will provide a highly important technical guarantee for the promotion and application of 6G technology. We sought to design a high-performance sensor with a large area, high responsiveness, and low equivalent noise power, which is stable at room temperature for long periods and still usable under high humidity; it is suitable for the environment of marine remote sensing technology and has the potential for mass production. We prepared a Te film with high stability and studied its crystallization method by comparing the sensing and detection effects of THz waves at different annealing temperatures. It is proposed that the best crystallization and detection effect is achieved by annealing at 100 °C for 60 min, with a sensitivity of up to 19.8 A/W and an equivalent noise power (NEP) of 2.8 pW Hz^−1/2. The effective detection area of the detector can reach the centimeter level, and this level is maintained for more than 2 months in a humid environment at 30 °C with 70–80% humidity and without encapsulation. Considering its advantages of stability, detection performance, large effective area, and easy mass preparation, our Te thin film is an ideal sensor for 6G ocean remote sensing technology. Full article

(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing)

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19 pages, 5063 KiB

Open AccessArticle

Application of a Randomized Algorithm for Extracting a Shallow Low-Rank Structure in Low-Frequency Reverberation

by Jie Pang and Bo Gao

Remote Sens. 2023, 15(14), 3648; https://doi.org/10.3390/rs15143648 - 21 Jul 2023

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Abstract

The detection performance of active sonar is often hindered by the presence of seabed reverberation in shallow water. Separating the reverberations from the target echo and noise in the received signal is a crucial challenge in the field of underwater acoustic signal processing. [...] Read more.

The detection performance of active sonar is often hindered by the presence of seabed reverberation in shallow water. Separating the reverberations from the target echo and noise in the received signal is a crucial challenge in the field of underwater acoustic signal processing. To address this issue, an improved Go-SOR decomposition method is proposed based on the subspace-orbit-randomized singular value decomposition (SOR-SVD). This method successfully extracts the low-rank structure with a certain striation pattern. The results demonstrate that the proposed algorithm outperforms both the original Go algorithm and the current state-of-the-art (SOTA) algorithm in terms of the definition index of the low-rank structure and computational efficiency. Based on the monostatic reverberation theory of the normal mode, it is established that the low-rank structure is consistent with the low-frequency reverberation interference striation. This study examines the interference characteristics of the low-rank structure in the experimental sea area and suggests that the interferences of the fifth and seventh modes mainly control the low-rank structure. The findings of this study can be applied to seafloor exploration, reverberation waveguide invariant (RWI) extraction, and data-driven reverberation suppression methods. Full article

(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing)

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18 pages, 4973 KiB

Open AccessArticle

A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery

by Xue Ji, Yi Ma, Jingyu Zhang, Wenxue Xu and Yanhong Wang

Remote Sens. 2023, 15(14), 3570; https://doi.org/10.3390/rs15143570 - 16 Jul 2023

Viewed by 1105

Abstract

Accurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with [...] Read more.

Accurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with extensive repetitive coverage. Many previous empirical SDB approaches are unsuitable for precision bathymetry mapping in various scenarios, due to the assumption of homogeneous bottom over the whole region, as well as the neglect of various interfering factors (e.g., turbidity) causing radiation attenuation. Therefore, this study proposes a bottom-type adaption-based SDB approach (BA-SDB). Under the consideration of multiple factors including suspended particulates and phytoplankton, it uses a particle swarm optimization improved LightGBM algorithm (PSO-LightGBM) to derive depth of each pre-segmented bottom type. Based on multispectral images of high spatial resolution and in situ observations of airborne laser bathymetry and multi-beam echo sounder, the proposed approach is applied in shallow water around Yuanzhi Island, and achieves the highest accuracy with an RMSE value of 0.85 m compared to log-ratio, multi-band, and classical machine learning methods. The results of this study show that the introduction of water-environment parameters improves the performance of the machine learning model for bathymetric mapping. Full article

(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing)

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11 pages, 8629 KiB

Open AccessTechnical Note

A PANN-Based Grid Downscaling Technology and Its Application in Landslide and Flood Modeling

by Binlan Zhang, Chaojun Ouyang, Dongpo Wang, Fulei Wang and Qingsong Xu

Remote Sens. 2023, 15(20), 5075; https://doi.org/10.3390/rs15205075 - 23 Oct 2023

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Abstract

The efficiency and accuracy of grid-based computational fluid dynamics methods are strongly dependent on the chosen cell size. The computational time increases exponentially with decreasing cell size. Therefore, a grid coarsing technology without apparent precision loss is essential for various numerical modeling methods. [...] Read more.

The efficiency and accuracy of grid-based computational fluid dynamics methods are strongly dependent on the chosen cell size. The computational time increases exponentially with decreasing cell size. Therefore, a grid coarsing technology without apparent precision loss is essential for various numerical modeling methods. In this article, a physical adaption neural network (PANN) is proposed to optimize coarse grid representation from a fine grid. A new convolutional neural network is constructed to achieve a significant reduction in computational cost while maintaining a relatively accurate solution. An application to numerical modeling of dynamic processes in landslides is firstly carried out, and better results are obtained compared to the baseline method. More applications in various flood scenarios in mountainous areas are then analyzed. It is demonstrated that the proposed PANN downscaling method outperforms other currently widely used downscaling methods. The code is publicly available and can be applied broadly. Computing by PANN is hundreds of times more efficient, meaning that it is significant for the numerical modeling of various complicated Earth-surface flows and their applications. Full article

(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing)

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