underwater mapping_2023

Underwater mapping 2023

08 - An End-to-End Underwater-Image-Enhancement Framework Based on Fractional Integral Retinex and Unsupervised Autoencoder.

Authors: Yang Yu, and Chenfeng Qin Underwater image enhancement faces challenges of light scattering, absorption, and distortion. Instead of using a specific underwater imaging model to mitigate the degradation of underwater images, the authors propose an end-to-end underwater-image-enhancement framework that combines fractional integral-based Retinex and an encoder–decoder network. The proposed variant of Retinex aims to alleviate haze and color distortion in the input image while preserving edges to a large extent by utilizing a modified fractional integral filter.

Authors: Zefeng Zhao, Zhuang Zhou, Yunting Lai, Tenghui Wang, Shujie Zou, Haohao Cai, and Haijun Xie Clear underwater images are necessary for many underwater applications, while absorption, scattering, and different water conditions will lead to blurring and different color deviations. This paper proposed a fusion- based image enhancement method for various water areas to overcome the limitations of the available color correction and deblurring algorithms. The authors proposed two novel image processing methods, namely, an adaptive channel deblurring method and a color correction method, by limiting the histogram mapping interval.

Authors: Shlomi Amitai, Itzik Klein, and Tali Treibitz Depth estimation is critical for any robotic system. In the past years, the depth estimation from monocular images has shown significant improvement; however, in the underwater environment, results are still lagging due to appearance changes caused by the medium. The authors suggest training using subsequent frames self-supervised by a reprojection loss, as demonstrated above water. The authors suggest several additions to the self-supervised framework to cope with the underwater environment and achieve state-of-the-art results on a challenging forward-looking underwater dataset.

Authors: Mochou Yang, Yi Wu, and Guoying Feng In an underwater environment, conventional imaging is limited by low sensitivity, resulting in fuzzy images, while ghost imaging can solve this problem. This study proposes underwater laser ghost imaging based on Walsh speckle patterns. According to the simulated and experimental results, noise resistance and a low sampling rate of ghost imaging based on Walsh speckle patterns are proved. As the underwater environment's turbidity increases, the image quality of ghost imaging based on Walsh speckle patterns decreases.

Authors: Zheyong Li, Jinghua Li, Pei Zhang, Lihui Zheng, Yilong Shen, Qi Li, Xin Li, and Tong Li The detection of underwater targets through hyperspectral imagery is a relatively novel topic as the assumption of target background independence is no longer valid, making it difficult to detect underwater targets using land target information directly. Meanwhile, deep-learning-based methods have faced challenges regarding the availability of training datasets, especially in underwater conditions. To solve these problems, a transfer-based framework is proposed in this paper, which exploits synthetic data to train deep-learning models and transfers them to real-world applications.

Authors: Yang Guan, Xiaoyan Liu, Zhibin Yu, Yubo Wang, Xingyu Zheng, Shaoda Zhang, and Bing Zheng Underwater image enhancement is a fundamental requirement in the field of underwater vision. Along with the development of deep learning, underwater image enhancement has made remarkable progress. However, most deep learning-based enhancement methods are computationally expensive, restricting their application in real-time large-size underwater image processing. The authors propose a model based on a generative adversarial framework for large-size underwater image enhancement to solve these problems.

Authors: Yelena Randall, and Tali Treibitz Yan The authors collected forward-looking stereo-vision and visual-inertial image sets with two underwater imaging platforms, a stereo camera rig, and an ROV in the Mediterranean and Red Sea. To their knowledge, there is only one other public dataset in the underwater environment with this camera-sensor orientation. These datasets are critical for developing several underwater applications, including autonomous obstacle avoidance, visual odometry, 3D tracking, Simultaneous Localization and Mapping (SLAM), and depth estimation through deep learning.

Authors: Yuanheng Li, Shengxiong Yang, Yuehua Gong, Jingya Cao, Guang Hu, Yutian Deng, Dongmei Tian, and Junming Zhou Unpaired Image-to-Image Translation using Cycle- Consistent Adversarial Networks, also known as CycleGAN, has been broadly studied regarding underwater image enhancement, but it is difficult to apply the model because it can be difficult to train if the dataset used is not appropriate. In this article, the authors devise a new method of building a dataset and choosing the best images based on the widely used Underwater Image Quality Measure (UIQM) scheme to create the dataset.

Authors: Shijie Xu, Rendong Feng, Pan Xu, Zhengliang Hu, Haocai Huang, and Guangming Li Underwater environment observation with underwater acoustic tomography has been considerably developed in recent years. Moving sound transmission can obtain the observation of entire spatial area with sound station moving. Various internal structures, unique surface and submarine boundaries and changing environment constitutes a complex acoustic propagation channel. This paper focus on the inversion method and signal resampling for sound moving transmission. Also, the current field in three-dimensional (3D) scale is also studied.

Authors: Marios Xanthidis, Bharat Joshi, Monika Roznere, Weihan Wang, Nathaniel Burgdorfer, Alberto Quattrini Li, Philippos Mordohai, Srihari Nelakuditi, and Ioannis Rekleitis The authors of this paper discuss how to effectively map an underwater structure with a team of robots considering the specific challenges posed by the underwater environment.

Authors: Shiying Feng, Xiaofeng Li, Lu Ren, Shuiqing Xu Autonomous navigation of unmanned aerial vehicles (UAVs) is widely used in building rescue systems. As the complexity of the task increases, traditional methods based on environment models are hard to apply. this paper proposes a reinforcement learning (RL) algorithm to solve the UAV navigation problem.

Authors: Daniel Short, Tingjun Lei, Chaomin Luo, Daniel W. Carruth, Zhuming Bi This study aims to address a gap in existing research by integrating a biologically inspired Bat Algorithm (BA) for path planning. It outlines the motivation behind the study, the methodology involving the use of convolutional neural networks (CNNs) for feature extraction, and the implementation of the BA for global path planning. It also highlights the structure of the paper and the validation of the proposed approach through simulations and comparisons.

Authors: Wei Song, Yaling Liu, Dongmei Huang, Bing Zhang,Zhihao Shen, & Huifang Xu This paper aims to systematically review underwater image restoration technology, bridging the gap between shallow-sea and deep-sea image restoration fields through experimental analysis. The authors first categorize shallow- sea image restoration methods into three types: physical model-based methods, prior-based methods, and deep learning-based methods that integrate physical models. The core concepts and characteristics of representative methods are analyzed. The research status and primary challenges in deep-sea image restoration are then summarized.

Authors: Xiaoyang Bai, Zuodong Liang, Zhongmin Zhu, Alexander Schwing, David Forsyth, & Viktor Gruev Monitoring water properties using autonomous underwater sampling robots remains a significant challenge due to a lack of underwater geolocalization capabilities. The authors present a new method for underwater geolocalization using deep neural networks trained on approximately 10 million polarization-sensitive images acquired globally, along with camera position sensor data.

Authors: Tuochao Chen, Justin Chan, & Shyamnath Gollakota The emergence of waterproof mobile and wearable devices designed for underwater activities opens up opportunities for underwater networking and localization capabilities on these devices. The authors present the first underwater acoustic positioning system for smart devices. Unlike conventional systems that use floating buoys as anchors at known locations, they design a system where a dive leader can compute the relative positions of all other divers, without any external infrastructure.

Authors: Xuanyao Bai, Kailun Wen, Donghong Peng, Shuangqiang Liu,and Le Luo The objective of this study is to explore the measurement principles and detection methods of atomic magnetometers, and it also examines the technological means and research progress of atomic magnetometers in various industrial fields, including magnetic imaging, material examination, underwater magnetic target detection, and magnetic communication. Additionally, this study discusses the potential applications and future development trends of atomic magnetometers.

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Authors: Xuebo Zhang , Haixin Sun, & Arata Kaneko This paper highlights the significance of marine environment and hydroacoustic data collection for military, economic, and ecological purposes. It discusses the challenges environmental interference poses to underwater communication and target detection and emphasizes the need for advanced technical solutions to improve detection and identification capabilities. The paper also underscores the importance of sound wave technology and signal processing in ocean exploration.

36 - Mapping Underwater Aquatic Vegetation Using Foundation Models With Air- and Space-Borne Images: The Case of Polyphytos Lake

Authors: Leonidas Alagialoglou, Ioannis Manakos, Sofia Papadopoulou, Rizos-Theodoros Chadoulis, & Afroditi Kita This study explains the effectiveness of different artificial intelligence (AI) techniques for mapping underwater aquatic vegetation (UVeg) using remote sensing data. It evaluates the performance of classical AI tools and a modern AI tool (Segment Anything Model, SAM) in the context of few-shot learning with limited annotation. The results highlight the superior performance of the SAM model for airborne imagery but also note its challenges for spaceborne imagery, providing researchers and practitioners with insights into the strengths and limitations of different AI models for UVeg mapping.

Authors: Shaowei Zhang, & Zhiqun Daniel Deng This document overviews the advancements and research in deep-sea exploration and observation technology. It highlights the development of sophisticated sensors and equipment for studying deep-sea environments, such as seabed samplers and MEMS sensors. The text also discusses specific technologies and methods, such as atmospheric evaporation waveguide communication and dual-axis rotary modulation inertial navigation systems, that enhance ocean exploration capabilities. Additionally, it mentions a study on evaporation duct properties in the East China Sea, emphasizing the importance of understanding this phenomenon for electromagnetic wave propagation.

Authors: Hualong Du, Huijie Fan, Qifeng Zhang, and Shuo Li This text describes a method for accurately measuring the volume of thick oil slicks in real-time, which is crucial for oil spill response strategies and evaluating disposal efficiency. It explains the use of ultrasonic inspection and image processing techniques, facilitated by a remotely operated vehicle and an airborne drone, to measure the thickness and area of oil slicks. It also highlights the successful verification of this method through laboratory experiments, suggesting its potential application in real- world oil spill scenarios.

Authors: Ziyang Wang, Liquan Zhao, Tie Zhong, Yanfei Jia, & Ying Cui This paper describes a proposed solution for improving the quality of underwater images using a generative adversarial network (GAN) with multi-scale and attention mechanisms. It explains the problem of degraded underwater images and introduces various modules and techniques integrated into the GAN to enhance image quality. It also mentions the evaluation of the proposed method using specific datasets and highlights the superior performance of the enhanced images compared to other methods.

Authors: Yiyang Chen, Pengqiang Ge, Guina Wang, Guirong Weng, Hongtian Chen This document is an analysis of the active contour model (ACM) approach in image segmentation within the field of computer vision. It reviews different types of ACMs, discuss their theoretical foundations, and evaluate their performance through experiments. Additionally, it seeks to compare ACMs with deep learning-based algorithms and identify future research directions in this area.

03 - Industry remotely operated vehicle imagery for assessing marine communities associated with subsea oil and gas infrastructure on the continental shelf of South-East Australia

Authors: Daniel Ierodiaconou, Dianne McLean, Matthew Jon Birt, Todd Bond, Sam Wines, Ollie Glade-Wright, Joe Morris, Doug Higgs, and Sasha K. Whitmarsh This study examines the ecological value of offshore oil and gas infrastructure in marine environments, particularly as these structures approach the end of their operational life. It informs decommissioning decisions by analyzing the diversity and distribution of fish, invertebrate, and benthic communities using remotely operated vehicle (ROV) imagery, and also provides an overview of the methods used, the results obtained, and highlights the ecological significance of certain species observed in the study.

Authors: Jiancheng Yin, Xiangyu You, Yu Yao This text informs the reader about the challenges and methods involved in detecting subsea oil pipelines, specifically focusing on the use of sonar and acoustic scattering characteristics. It also aims to describe a study that establishes an acoustic scattering model using the finite element method to analyze various factors affecting the detection of buried pipelines.

Authors: Zefeng Zhao, Zhuang Zhou, Yunting Lai, Tenghui Wang, Shujie Zou, Haohao Cai, and Haijun Xie This text presents findings on the effectiveness of different technologies, specifically the multibeam echo sounder (MBES) WASSP S3 and the remotely operated underwater vehicle (ROV) Chasing M2, in detecting and mapping marine litter on the sea floor. It evaluates the capabilities and limitations of these technologies in identifying marine debris, particularly in the St. Ante Channel area in Croatia, and provides guidelines for their integrated use in marine litter detection.

Authors: Shuangshuang Fan, Xinyu Zhang, Guangxian Zeng, and Xiao Cheng This study aims to improve the efficiency and accuracy of mapping the underside of sea ice by proposing adaptive sampling and map reconstruction methods. It highlights the challenges of mapping ice topography with limited resources and introduces a graphics-based adaptive mapping method and a sparse approximation method for reconstructing ice draft maps. The intent is to contribute to the scientific understanding of sea-ice interactions and provide a cost-effective solution for under-ice observation missions.

Authors: Ahila Priyadharshini R, Ramajeyam K This study introduces a new methodology for enhancing the visual quality of underwater images. It outlines the challenges faced in underwater imaging, such as speckle noise, backscatter noise, and blur, and explains how the proposed approach, which includes techniques like color correction, contrast enhancement, homomorphic filtering, and fusion, addresses these issues. It also highlights the effectiveness of the methodology through qualitative and quantitative assessments, claiming it surpasses existing state-of-the-art techniques. The ultimate goal is to improve the quality of underwater images for fields like marine biology, oceanography, and underwater archaeology.

09 - Underwater ice adaptive mapping and reconstruction using autonomous underwater vehicles.

Authors: Shuangshuang Fan, Xinyu Zhang, Guangxian Zeng, and Xiao Cheng The undersides of floating ice shelves and sea ice in Antarctica and the Arctic are hard to access. Understanding the interactions between ice shelves, sea ice, and the ocean is important. Mapping the underside of sea ice is necessary to understand its growth and decay. This paper presents a low-cost underwater ice mapping method for small Autonomous Underwater Vehicles (AUVs) using adaptive sampling and reconstruction methods. The proposed methods are validated with field data.

06 - Weibull Tone Mapping (WTM) for the Enhancement of Underwater Imagery

Authors: Chloe Amanda Game, Michael Barry Thompson, and Graham David Finlayson This paper presents a simpler and faster interactive tone- mapping approach for end-users, built upon Weibull Tone Mapping theory. It allows users to shape brightness distributions using two parameter sliders. Experiments show that this method can make desired adjustments in over 80% of images, while 91% of control-point TMOs can be visually well-approximated. The work emphasizes the importance of image purpose in underwater image enhancement.

18 - Underwater terrain positioning method based on Markov random field for unmanned underwater vehicles

Authors: Pengyun Chen, Ying Liu, Xiaolong Chen, Teng Ma, and Lei Zhang This paper presents a Markov random field model for underwater terrain-matching positioning, based on real- time terrain data from a multi-beam echo sounder. The method improves terrain adaptability and accuracy, and its playback simulation tests show its usability in underwater engineering applications for positioning correction.

28 - Enhancement and Optimization of Underwater Images and Videos Mapping

Authors: Chengda Li, Xiang Dong, Yu Wang, and Shuo Wang This paper presents a high-speed enhancement and restoration method for underwater images and videos based on the dark channel prior (DCP). It includes an improved background light estimation method, rough estimation of the R channel's transmission map, a TM optimizer, and an improved color correction algorithm. The method is tested using various image-quality assessment indexes and real-time underwater video measurements on a flipper-propelled underwater vehicle- manipulator system.

30 - Recent advances in synthetic aperture sonar technology

Authors: Xuebo Zhang, Gary Heald, Anthony P. Lyons, Roy Edgar Hansen, and Alan J. Hunter Synthetic aperture sonar (SAS) and interferometric synthetic aperture sonar (InSAS) provide higher resolution images than traditional sidescan sonar, attracting interest in underwater engineering. Advances in navigation, autonomous vehicles, and sonar technology have led to lighter, more efficient SAS/InSAS systems. This Special Issue includes research and studies on recent developments and applications in SAS/InSAS technology.

Authors: Feihu Zhang, Diandian Xu and Chensheng Cheng Multi-vehicle collaborative mapping is more efficient in unfamiliar underwater environments than single-vehicle methods. The challenge of Simultaneous Localization and Mapping (SLAM) with multiple underwater vehicles is map registration. An algorithm using the Gaussian Mixture Robust Branch and Bound (GMRBnB) algorithm and interior point filtering technique is proposed, which improves outlier tolerance and map registration accuracy, surpassing Iterative Closest Point and Normal Distributions Transform methods.

42 - An Underwater Distributed SLAM Approach Based on Improved GMRBnB Framework

07 - Investigating the rate of turbidity impact on underwater spectral reflectance detection

Authors: Hong Song, Syed Raza Mehdi, Zixin Li, Mengjie Wang, Chaopeng Wu, Vladimir Yu Venediktov, and Hui Huang This document explores the inherent optical properties of targeted objects, emphasizing the need to understand how turbidity influences spectral data collection. Utilizing a stare-type underwater spectral imaging system with a liquid crystal tunable filter, the research examines the correlation between increasing turbidity levels and scattering intensity. The findings demonstrate a notable increase in scattering effects at specific wavelengths and highlight the effect of turbidity on spectral detection accuracy, offering insights for improving underwater object detection techniques.

04 - Using UAVs and Photogrammetry in Bathymetric Surveys in Shallow Waters - Published by MDPI - Applied Sciences

Authors: Alexandre Almeida Del Savio, Ana Luna Torres, Monica Alejandra Vergara Olivera, Sara Rocio Llimpe Rojas, , Gianella Tania Urday Ibarra, and Alcindo Neckel This article discusses the use of unmanned aerial vehicles (UAVs) and photogrammetry for conducting bathymetric surveys with enhanced safety, cost-effectiveness, and accuracy. The study evaluates error levels during UAV flights over controlled water bodies at varying depths, analyzing how turbidity and luminosity influences the accuracy of these measurements. Key findings indicate that higher luminosity correlates with lower errors up to a depth of 0.97 m, while the effect of turbidity on error rates varies based on depth, suggesting practical applications for UAV-based photogrammetry in shallow waters.

05 - Photogrammetry, from the Land to the Sea and Beyond: A Unifying Approach to Study Terrestrial and Marine Environments - Published by MDPI - Journal of Marine Science and Engineering

Authors: Torcuato Pulido Mantas, Camilla Roveta, Barbara Calcinai, Cristina Gioia di Camillo, Chiara Gambardella, Chiara Gregorin, Martina Coppari, Teo Marrocco, Stefania Puce, Agnese Riccardi, and Carlo Cerrano This article chronicles the evolution of photogrammetry, particularly focusing on the structure from motion (SfM) technique that has gained popularity due to technological advances over two decades. It highlights the transition of this methodology from terrestrial settings to underground and underwater surveys, noting that recent affordable imaging systems have democratized access to these techniques across various research fields

43 - Workshop on 3D mapping of habitats and biological communities with underwater photogrammetry - Published by Research Ideas and Outcomes (RIO)

Authors: Loïc Van Audenhaege, Vincent Mahamadaly, David Price, Alexandre Sneessens, Hayley C. Cawthra, Clément Delamare, Valentin Danet, Simon Delsol, Rodolphe Devillers, Iason-Zois Gazis, Isabel Urbina-Barreto This paper discusses the growing use of photogrammetry for monitoring underwater structures and its development as a data collection method in aquatic environments. It highlights a workshop organized at the annual GeoHab conference aimed at addressing the audience’s lack of knowledge in photogrammetry. The workshop included theoretical concepts, sampling designs, practical case studies, and hands-on training in data acquisition and processing.

45 - Low-Tech and Low-Cost System for High-Resolution Underwater RTK Photogrammetry in Coastal Shallow Waters - Published by MDPI - Remote sensing

Authors: Marion Jaud, Simon Delsol, Isabel Urbina-Barreto , Emmanuel Augereau, Emmanuel Cordier, François Guilhaumon, Nicolas Le Dantec, France Floc’h, and Christophe Delacourt This study presents a novel, low-cost system named POSEIDON, which utilizes Structure-from-Motion (SfM) photogrammetry to create high-resolution 3D models of the seabed. With a cost of approximately USD 1500, this adaptable prototype includes floating support, imagery sensors, and a precise positioning system. Validation of POSEIDON’s methodology reveals a mean deviation of 5.2 cm compared to traditional terrestrial surveys, highlighting its potential applications in various scientific and technical fields.

17 - Combining drone and underwater photogrammetry to map coral reef complexity at centimeter resolution over large extents

Authors: Urbina-Barreto Isabel, Poulain Sylvain, Chauvin Anne, Lelabousse Clément, Barde Julien, Tribollet Aline, Guilhaumon François This study presents a novel methodological framework that integrates aerial drone (UAV) photogrammetry with underwater photogrammetry to create high-resolution, seamless 3D models of coral reef ecosystems. By combining above-water and underwater imagery, the authors reconstruct reef structures at centimeter-level resolution across large areas. Therefore, it enables precise quantification of 3D habitat complexity metrics across entire reefscapes.

Authors: Bin Hu, Yiqiang Zhao, Guoqing Zhou, Jiaji He, Changlong Liu,, Qiang Liu, Mao Ye, and Yao Li Underwater laser detection is hindered by light attenuation from scattering and absorption in water. This study designs an underwater Lidar system using a paraxial multi-channel detection strategy to improve dynamic range in subsea environments. A multi-channel underwater Lidar simulation (MULS) method based on radiative transfer Lidar equations was developed and validated through experiments under varied water conditions. The prototype achieved range accuracy better than 0.1085 m, with strong correlations between simulated and measured waveforms, confirming the simulation method's reliability.

29 - Simulation and Design of an Underwater LiDAR System Using Non-Coaxial Optics and Multiple Detection Channels

Authors: Yongqiang Chen, Shouchuan Guo, Yan He, Yuan Luo, Weibiao Chen, Shanjiang Hu, Yifan Huang, Chunhe Hou, and Sheng Su Underwater laser detection is hindered by light attenuation from scattering and absorption in water. This study introduces an underwater Lidar system using a paraxial multi-channel detection strategy to improve dynamic range in subsea environments. A multi-channel underwater Lidar simulation (MULS) method, based on radiative transfer Lidar equations, was developed and validated through experiments under varied water conditions. The prototype achieved range accuracy better than 0.1085 m per channel, with strong agreement between simulated and measured waveforms, confirming the simulation's reliability.

Author: Guangbo Xu Underwater LiDAR performance is hindered by laser attenuation and backscattering from suspended particles. To overcome these limitations, this paper presents a high- speed signal processing system for underwater LiDAR utilizing complete waveform sampling. The system features a photodiode preamplifier and a 2 GHz sampling frequency main control board, enabling a ranging accuracy of 0.075 m and backscatter filtering. This work offers a valuable reference for underwater LiDAR signal processing system design.

Authors: Pau Vial, Narcís Palomeras, Joan Solà, Marc Carreras This paper presents a two-dimensional Simultaneous Localization and Mapping (SLAM) system for Autonomous Underwater Vehicles (AUVs). Designed to overcome the challenges of the underwater environment, the system utilizes inertial sensors and a mechanical profiling sonar. Key innovations include a dead reckoning system based on Lie Theory to manage pose uncertainty and a rigid scan matching technique adapted for acoustic data, which also estimates matching uncertainty. Bayesian- Gaussian mixtures are employed in scan matching, with registration solved via optimization in Lie groups.

Authors: Xudong Liu, Liping Zhang, Xiaoyu Zhai, Liye Li, Qingji Zhou, Xue Chen, and Xiaobo Li Polarization lidar (P-lidar) advances traditional lidar by incorporating polarization properties (degree, angle, ellipticity) alongside backscattering intensity and spectrum. This expands detected physical information, enhancing analysis for atmospheric, oceanic, and terrestrial remote sensing. This review introduces P-lidar principles and systems, surveys its applications across these domains, and proposes future research directions to stimulate further development.

44 - Handheld liDAR as a tool for characterizing wood-rich river Corridors - Published by Wiley

Authors: Anna Marshall, Ryan R. Morrison, Brady Jones, Shayla Triantafillou, Ellen Wohl Wood accumulations significantly affect geomorphic, hydraulic, and ecological processes in river corridors, yet accurately characterizing them remains challenging. This study assesses the effectiveness of handheld lidar scanners, specifically the fourth-generation iPad Pro, in collecting 3D wood accumulation data for measuring volume, porosity, complexity, and roughness. Findings indicate that handheld lidar offers a cost-effective alternative to traditional field methods by providing user- friendly data collection and visualization, enabling accurate temporal volume comparisons, aiding porosity measurements, and supporting hydraulic and morphodynamic modeling.