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Advancement in Undersea Remote Sensing II
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Advancement in Undersea Remote Sensing II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 9877

Special Issue Editors

Dr. Bing Ouyang

E-Mail Website
Guest Editor
Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, FL, USA
Interests: underwater imaging applications; computer vision in underwater laser imaging applications; real-time environmental monitoring and events detection; application of electro-optic imaging numerical model and deconvolution technique in image enhancement and pulse resolution improvements
Special Issues, Collections and Topics in MDPI journals
Dr. Fraser Dalgleish

E-Mail Website
Guest Editor
L3Harris Technologies, Space & Airborne Systems, NASA Boulevard, Melbourne, FL 32919, USA
Interests: underwater imaging applications; computer vision in underwater laser imaging applications; real-time environmental monitoring and events detection; application of electro-optic imaging numerical model and deconvolution technique in image enhancement and pulse resolution improvements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Gaining a better understanding of the marine environment has been a primary aim for humanity going back to the ancient times. However, it is only over the last several decades, enabled by the ongoing microelectronics and computer technological revolution, that significant progress has been made to develop the platforms, sensors, and other related technologies to overcome the opaque barrier between humans and the underwater world. Indeed, our desire to explore the ocean has recently spawned a plethora of advanced undersea remote sensing techniques and technologies that are still growing exponentially, and this Special Issue will be focused on compiling a balanced collection of papers that detail the most recent advancements in this area.

Submissions are hereby invited for original research, review articles and case studies that are new contributions in the advancement of underwater remote sensing. Theoretical and experimental contributions, original and review studies, and industrial and university research is welcome.

The main topics of interest include, but are not limited to, the following:

  • Underwater robotics and platforms;
  • Underwater sonar technology;
  • Underwater optical and acoustical communications;
  • Underwater lidar sensors and imagers;
  • Underwater signal processing and image enhancements;
  • Underwater turbulence sensing;
  • Marine species detection and identification;
  • Aquaculture monitoring systems;
  • Machine learning for undersea remote sensing.

Dr. Bing Ouyang
Dr. Fraser Dalgleish
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • underwater robotics
  • undersea remote sensing
  • underwater lidar
  • machine learning
  • aquaculture
  • marine species detection

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Related Special Issue

Published Papers (6 papers)

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Research

18 pages, 3211 KiB  
Article
S3DR-Det: A Rotating Target Detection Model for High Aspect Ratio Shipwreck Targets in Side-Scan Sonar Images
by Quanhong Ma, Shaohua Jin, Gang Bian, Yang Cui, Guoqing Liu and Yihan Wang
Remote Sens. 2025, 17(2), 312; https://doi.org/10.3390/rs17020312 - 17 Jan 2025
Viewed by 714
Abstract
The characteristics of multi-directional rotation and high aspect ratio of targets such as shipwrecks lead to low detection accuracy and difficulty localizing existing detection models for this target type. Through our research, we design three main inconsistencies in rotating target detection compared to [...] Read more.
The characteristics of multi-directional rotation and high aspect ratio of targets such as shipwrecks lead to low detection accuracy and difficulty localizing existing detection models for this target type. Through our research, we design three main inconsistencies in rotating target detection compared to traditional target detection, i.e., inconsistency between target and anchor frame, inconsistency between classification features and regression features, and inconsistency between rotating frame quality and label assignment strategy. In this paper, to address the discrepancies in the above three aspects, we propose the Side-scan Sonar Dynamic Rotating Target Detector (S3DR-Det), which is a model with a dynamic rotational convolution (DRC) module designed to effectively gather rotating targets’ high-quality features during the model’s feature extraction phase, a feature decoupling module (FDM) designed to distinguish between the various features needed for regression and classification in the detection phase, and a dynamic label assignment strategy based on spatial matching prior information (S-A) specific to rotating targets in the training phase, which can more reasonably and accurately classify positive and negative samples. The three modules not only solve the problems unique to each stage but are also highly coupled to solve the difficulties of target detection caused by the multi-direction and high aspect ratio of the target in the side-scan sonar image. Our model achieves an average accuracy (AP) of 89.68% on the SSUTD dataset and 90.19% on the DNASI dataset. These results indicate that our model has excellent detection performance. Full article
(This article belongs to the Special Issue Advancement in Undersea Remote Sensing II)
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25 pages, 7113 KiB  
Article
Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems
by Alisa Kunapinun, William Fairman, Paul S. Wills, Dennis Hanisak and Bing Ouyang
Remote Sens. 2024, 16(23), 4418; https://doi.org/10.3390/rs16234418 - 26 Nov 2024
Viewed by 994
Abstract
Monitoring seaweed growth rates and biomass is crucial for optimizing harvest strategies in aquaculture systems. While such a task can be managed manually on a small farm with a few seaweed tanks, it is not feasible on a commercial farm with 1000s of [...] Read more.
Monitoring seaweed growth rates and biomass is crucial for optimizing harvest strategies in aquaculture systems. While such a task can be managed manually on a small farm with a few seaweed tanks, it is not feasible on a commercial farm with 1000s of tanks. To this end, an Internet of Things (IoT) based seaweed growth monitoring system is being developed at Harbor Branch Oceanographic Institute (HBOI) at Florida Atlantic University (FAU). Using the Integrated Multi-Trophic Aquaculture (IMTA) system at HBOI as the test site, the project aims to develop a solution that allows farm managers to monitor seaweed growth remotely using automated sensors. An important component of this IoT solution is the machine learning-based prediction model. This study introduces an advanced Long Short-Term Memory (LSTM)-based approach for forecasting seaweed growth and biomass. In particular, an algae growth mathematical model driven by readily available environmental and aquaculture conditions has been integrated as a physical constraint in the LSTM model. This design addresses a principal challenge in this study—the lack of continuous ground truth measurements, as the biomass is recorded only at discrete intervals (e.g., initial, weekly partial harvests, and final harvest). The LSTM models are trained and evaluated for their predictive performance using experimental and synthetic data. Compared with the LSTM models with MSE loss function alone, the results showed that the model with a loss function under physics constraint achieved a significantly lower error in predicting seaweed growth. The rationale behind choosing LSTM over other state-of-the-art models is presented in the paper. This study highlights the potential of integrating machine learning with physical models to optimize seaweed cultivation and support sustainable aquaculture practices. The proposed methodology can seamlessly extend to the remote sensing data in other aquaculture settings. Full article
(This article belongs to the Special Issue Advancement in Undersea Remote Sensing II)
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20 pages, 7688 KiB  
Article
System Design of Ocean Temperature Measurement System Using Brillouin Lidar Based on Dual Iodine Cells
by Fu Yang, Wenhao Chen, Luqiang Liang, Chunqi Fang and Yan He
Remote Sens. 2024, 16(15), 2748; https://doi.org/10.3390/rs16152748 - 27 Jul 2024
Viewed by 892
Abstract
Ocean temperature profile information plays a key role in understanding the marine environment. The passive remote-sensing technique can provide sea surface temperature measurements over large areas. However, it is sensitive to the atmospheric environment and cannot provide seawater temperature profile information. The lidar [...] Read more.
Ocean temperature profile information plays a key role in understanding the marine environment. The passive remote-sensing technique can provide sea surface temperature measurements over large areas. However, it is sensitive to the atmospheric environment and cannot provide seawater temperature profile information. The lidar technique is the only way to carry out seawater temperature profile measurements over large areas. However, it is insufficient for measuring speed, the receiving field, stability, spectral integrity, simple system structures, and so on. Therefore, we propose a Brillouin lidar method combining two iodine cells at different temperatures to realize temperature measurements, where one iodine cell is used as a filter to absorb the elastic scattering and the other as an edge detection discriminator to obtain the seawater temperature measurement. The system has a fast measurement speed, a large receiving field, a simple system structure, and high stability. The system feasibility was verified via principle simulation and real iodine absorption curve measurements. For an ocean temperature of [5 °C, 15 °C], a laser wavelength of 532.10495 nm was more appropriate, corresponding to the iodine pool temperature combinations of 50 °C and 78 °C. For an ocean temperature of [15 °C, 32 °C], a laser wavelength of 532.10518 nm was more appropriate, corresponding to the iodine cell temperature combinations of 60 °C and 78 °C. When the laser intensity reached a measurement precision of 1‰, the temperature could be predicted with an accuracy of up to 0.2 K. This work shows promise as a potential solution for seawater temperature profile measurement. Full article
(This article belongs to the Special Issue Advancement in Undersea Remote Sensing II)
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20 pages, 78594 KiB  
Article
Underwater Side-Scan Sonar Target Detection: YOLOv7 Model Combined with Attention Mechanism and Scaling Factor
by Xin Wen, Jian Wang, Chensheng Cheng, Feihu Zhang and Guang Pan
Remote Sens. 2024, 16(13), 2492; https://doi.org/10.3390/rs16132492 - 8 Jul 2024
Cited by 4 | Viewed by 2955
Abstract
Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, [...] Read more.
Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model’s focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model’s accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy (mAP0.5) and (mAP0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images. Full article
(This article belongs to the Special Issue Advancement in Undersea Remote Sensing II)
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22 pages, 14988 KiB  
Article
Channel Estimation for Underwater Acoustic Communications in Impulsive Noise Environments: A Sparse, Robust, and Efficient Alternating Direction Method of Multipliers-Based Approach
by Tian Tian, Kunde Yang, Fei-Yun Wu and Ying Zhang
Remote Sens. 2024, 16(8), 1380; https://doi.org/10.3390/rs16081380 - 13 Apr 2024
Cited by 1 | Viewed by 1657
Abstract
Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an [...] Read more.
Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l1−l1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method. Full article
(This article belongs to the Special Issue Advancement in Undersea Remote Sensing II)
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25 pages, 17753 KiB  
Article
A Dual-Branch Autoencoder Network for Underwater Low-Light Polarized Image Enhancement
by Chang Xue, Qingyu Liu, Yifan Huang, En Cheng and Fei Yuan
Remote Sens. 2024, 16(7), 1134; https://doi.org/10.3390/rs16071134 - 24 Mar 2024
Cited by 3 | Viewed by 1695
Abstract
Underwater detection faces uncomfortable illumination conditions, and traditional optical images sensitive to intensity often cannot work well in these conditions. Polarization imaging is a good solution for underwater detection under adverse lighting conditions. However, the process of obtaining polarization information causes it to [...] Read more.
Underwater detection faces uncomfortable illumination conditions, and traditional optical images sensitive to intensity often cannot work well in these conditions. Polarization imaging is a good solution for underwater detection under adverse lighting conditions. However, the process of obtaining polarization information causes it to be more sensitive to noise; serious noise reduces the quality of polarized images and subsequent performance in advanced visual tasks. Unfortunately, the flourishing low-light image enhancement methods applied to intensity images have not demonstrated satisfactory performance when transferred to polarized images. In this paper, we propose a low-light image enhancement paradigm based on the antagonistic properties of polarization parameters. Furthermore, we develop a dual-branch network that relies on a gradient residual dense feature extraction module (GRD) designed for polarized image characteristics and polarization loss, effectively avoiding noise introduced during the direct amplification of brightness, and capable of restoring target contour details. To facilitate a data-driven learning method, we propose a simulation method for underwater low-light polarized images. Extensive experimental results on real-world datasets demonstrate the effectiveness of our proposed approach and its superiority against other state-of-the-art methods. Full article
(This article belongs to the Special Issue Advancement in Undersea Remote Sensing II)
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