Academic literature on the topic 'Side-scan sonar'

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Journal articles on the topic "Side-scan sonar"

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Zheng, Ye, Jun Yan, Junxia Meng, and Ming Liang. "A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8." Applied Sciences 15, no. 5 (2025): 2396. https://doi.org/10.3390/app15052396.

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Because of their low cost and ease of deployment, side-scan sonars is one of the most widely used underwater survey instruments. However, the complexity of the marine environment and the difficulty in target acquisition limit the detection accuracy of side-scan sonars. To address these issues, this study proposes a small-sample target detection method of side-scan sonar images using the Cycle-Consistent Generative Adversarial Network (CycleGAN) model and the improved YOLOv8 model. First, considering the difficulty in obtaining side-scan sonar target images, the proposed method uses the CycleGA
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Ye, Xiufen, Haibo Yang, Chuanlong Li, Yunpeng Jia, and Peng Li. "A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex." Remote Sensing 11, no. 11 (2019): 1281. http://dx.doi.org/10.3390/rs11111281.

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When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale corr
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Maccabee, Bruce S., and Charles E. Bell. "Bistatic side scan sonar." Journal of the Acoustical Society of America 92, no. 1 (1992): 626. http://dx.doi.org/10.1121/1.404084.

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Daniel, S., F. Le Leannec, C. Roux, B. Soliman, and E. P. Maillard. "Side-scan sonar image matching." IEEE Journal of Oceanic Engineering 23, no. 3 (1998): 245–59. http://dx.doi.org/10.1109/48.701197.

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Anderson, Aubrey L., and Shufa Dwan. "Simulation of side‐scan sonar." Journal of the Acoustical Society of America 80, S1 (1986): S112. http://dx.doi.org/10.1121/1.2023566.

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Thorpe, S. A., and A. J. Hall. "Nearshore Side-Scan Sonar Studies." Journal of Atmospheric and Oceanic Technology 10, no. 5 (1993): 778–83. http://dx.doi.org/10.1175/1520-0426(1993)010<0778:nssss>2.0.co;2.

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Maccabee, Bruce, and Charles E. Bell. "4975887 Bistatic side scan sonar." Deep Sea Research Part B. Oceanographic Literature Review 38, no. 8 (1991): 701–2. http://dx.doi.org/10.1016/s0198-0254(06)80684-9.

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Ge, Qiang, Fengxue Ruan, Baojun Qiao, Qian Zhang, Xianyu Zuo, and Lanxue Dang. "Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks." Electronics 10, no. 15 (2021): 1823. http://dx.doi.org/10.3390/electronics10151823.

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Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar
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Yang, Zhiwei, Jianhu Zhao, Hongmei Zhang, Yongcan Yu, and Chao Huang. "A Side-Scan Sonar Image Synthesis Method Based on a Diffusion Model." Journal of Marine Science and Engineering 11, no. 6 (2023): 1103. http://dx.doi.org/10.3390/jmse11061103.

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The limited number and under-representation of side-scan sonar samples hinders the training of high-performance underwater object detection models. To address this issue, in this paper, we propose a diffusion model-based method to augment side-scan sonar image samples. First, the side-scan sonar image is transformed into Gaussian distributed random noise based on its a priori discriminant. Then, the Gaussian noise is modified step by step in the inverse process to reconstruct a new sample with the same distribution as the a priori data. To improve the sample generation speed, an accelerated en
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Wen, Xin, Jian Wang, Chensheng Cheng, Feihu Zhang, and Guang Pan. "Underwater Side-Scan Sonar Target Detection: YOLOv7 Model Combined with Attention Mechanism and Scaling Factor." Remote Sensing 16, no. 13 (2024): 2492. http://dx.doi.org/10.3390/rs16132492.

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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 detect
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Dissertations / Theses on the topic "Side-scan sonar"

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Le, Bas Timothy P. "Processing techniques for TOBI side-scan sonar data." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360112.

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Beattie, Robert Scott. "Side scan sonar image formation, restoration and modelling." Thesis, Robert Gordon University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318551.

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Ulloa, Marco Julio. "Side-scan sonar observations of upper ocean processes." Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400484.

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Mignotte, Pierre-Yves. "Content-based registration of side-scan sonar images." Thesis, Heriot-Watt University, 2008. http://hdl.handle.net/10399/2189.

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This thesis is devoted to the image registration problem of multi-modal images with applications to underwater imagery. With the advent of Autonomous Underwater Vehicles, high resolution images of the seabed can be routinely acquired. However, the relative short range of the sensors implies that a mosaicing process is necessary to produce large scale maps.
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Fox, Peter. "An electronically focused multiple beam side scan sonar." Doctoral thesis, University of Cape Town, 1985. http://hdl.handle.net/11427/26521.

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Phased arrays have been in use since World War II but their commercial application has been constrained by the bulk and cost of the beam forming system. High resolution side scan sonar systems have many operational parameters that may only be extended with the aid of phased arrays, the resolution and the imaging rate being the most important. This thesis describes a microprocessor controlled dynamically focused side scan sonar where high resolution and high image acquisition rates are achieved. Dynamic focusing prevents the depth of field limitations of fixed focus arrays by updating the array
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Högberg, Lennarth. "Det dolda kulturlandskapet : Okända fartygslämningar i vår närmiljö." Thesis, Uppsala universitet, Institutionen för arkeologi och antik historia, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-452755.

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Along Sweden’s east coast there are thousands of well-preserved shipwrecks after an extensive shipping that stretches far back in history. The Baltic Sea is a unique sea in the sense that the ship worm Teredo Navalis cannot live here, which means that the ship’s timber is well preserved under water. But we have little knowledge how many these wrecks are and where they are located. No comprehensive underwater inventory like those made on land has even been made. Therefore, these shipwrecks are threatened with destruction by our ignorance of them. By scanning water areas in our immediate environ
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Ling, Li. "Local Feature Correspondence on Side-Scan Sonar Seafloor Images." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291803.

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In underwater environments, the perception and navigation systems are heavily dependent on the acoustic wave based sonar technology. Side-scan sonar (SSS) provides high-resolution, photo-realistic images of the seafloor at a relatively cheap price. These images could be considered potential candidates for place recognition and navigation of autonomous underwater vehicles (AUVs). Local feature correspondence matching, or the detection, description and matching of keypoints in overlapping images is a necessary building block for AUV navigation. Recent deep learning based research has resulted in
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Hamill, Daniel. "Quantifying Riverbed Sediment Using Recreational-Grade Side Scan Sonar." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6635.

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The size and organization of bed material, bed texture, is a fundamental attribute of channels and is one component of the physical habitat of aquatic ecosystems. Multiple discipline-specific definitions of texture exist and there is not a universally accepted metric(s) to quantify the spectrum of possible bed textures found in aquatic environments. Moreover, metrics to describe texture are strictly statistical. Recreational-grade side scan sonar systems now offer the possibility of imaging submerged riverbed sediment at resolutions potentially sufficient to identify subtle changes in bed text
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Xie, Yiping. "Machine Learning for Inferring Depth from Side-scan Sonar Images." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264835.

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Underwater navigation using Autonomous Underwater Vehicles (AUVs), which is significant for marine science research, highly depends on the acoustic method, sonar. Typically, AUVsare equipped with side-scan sonars and multibeam sonars at the same time since they both have their advantages and limitations. Side-scan sonars have a much wider range than multibeamsonars and at the same time are much cheaper, yet they could not provide accurate depth measurements. This thesis is aiming at investigating if a machine-interpreted method could beused to translate side-scan sonar data to multibeam data w
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Humber, Stuart Richard. "High resolution side-scan sonar imaging of coral reefs in the Seychelles." Thesis, Imperial College London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408036.

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Books on the topic "Side-scan sonar"

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Mazel, Charles. Side scan sonar training manual. Klein Associates, 1985.

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Fish, John Perry. Sound reflections: Advanced applications of side scan sonar. Lower Cape Pub., 2001.

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W, Nicholson John. Registration and variability of side scan sonar imagery. Woods Hole Oceanographic Institution, 1988.

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Clausner, James E. Side-scan sonar applications for evaluating coastal structures. U.S. Army Engineer Waterways Experiment Station, 1988.

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Clausner, James E. Side-scan sonar applications for evaluating coastal structures. U.S. Army Engineer Waterways Experiment Station, 1988.

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J, Mienert, and Weaver P. P. E, eds. European margin sediment dynamics: Side-scan sonar and seismic images. Springer, 2003.

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Daniel Távora de Queiroz Cobra. Estimation and correction of geometric distortions in side-scan sonar design. WHOI, 1990.

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Fish, John Perry. Sound underwater images: A guide to the generation and interpretation of side scan sonar data. Lower Cape Pub., 1990.

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Paskevich, Valerie F. Digital mapping of side-scan sonar data with the Woods Hole Image Processing System software. U.S. Dept. of the Interior, Geological Survey, 1992.

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Geological Survey (U.S.), ed. Digital mapping of side-scan sonar data with the Woods Hole Image Processing System software. U.S. Dept. of the Interior, Geological Survey, 1992.

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Book chapters on the topic "Side-scan sonar"

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Klein, Martin. "Side Scan Sonar." In International Handbook of Underwater Archaeology. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0535-8_39.

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Klein, Martin. "New Capabilities for Side-Scan Sonar." In Maritime Archaeology. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4899-0084-5_40.

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Alais, Pierre, Pascal Challande, François Ollivier, and Nicolas Cesbron. "A New Generation Side Scan Sonar." In Acoustical Imaging. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2958-3_96.

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Morales, Juan A., and Irene Delgado. "Side-Scan Sonar Imaging of Sediment Bedload." In Encyclopedia of Estuaries. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-8801-4_162.

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Khidkikar, Mahesh, and Ramprasad Balasubramanian. "Segmentation and Classification of Side-Scan Sonar Data." In Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33509-9_36.

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Wu, Ziyin, Fanlin Yang, and Yong Tang. "Side-scan Sonar and Sub-bottom Profiler Surveying." In High-resolution Seafloor Survey and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9750-3_4.

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Thorpe, S. A., A. Graham, and A. Hall. "Wave and dispersion studies in shallow water using side-scan sonar." In Mixing in Estuaries and Coastal Seas. American Geophysical Union, 1996. http://dx.doi.org/10.1029/ce050p0110.

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Taylor, J., J. A. Dowdeswell, N. H. Kenyon, R. J. Whittington, and J. Mienert. "Long-Range Side-Scan Sonar Imagery of the North Faeroes Margin." In European Margin Sediment Dynamics. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55846-7_21.

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Li, Yuhui, Zexin Guo, Junyi Wang, and Jun Fu. "FSONet: Side-Scan Sonar Image Recognition Based on Feature Space Optimization." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2228-3_30.

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Asada, Miho, Kyoko Okino, Hisashi Koyama, Akira Asada, and Tamaki Ura. "Brief Report of Side-Scan Sonar Observations Around the Yokoniwa NTO Massif." In Subseafloor Biosphere Linked to Hydrothermal Systems. Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54865-2_35.

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Conference papers on the topic "Side-scan sonar"

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Zhang, Liheng, Siquan Yu, and Lei Gao. "Side Scan Sonar Image Alignment for Complex Terrain." In 2024 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2024. https://doi.org/10.1109/icus61736.2024.10839967.

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Yang, Yiqiao, Chenglin Pang, Chengdong Wu, and Zheng Fang. "Geometry-aided Underwater 3D Mapping Using Side-scan Sonar." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801520.

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Hoff, Simon, Vegard Haraldstad, Bjørnar Reitan Hogstad, and Damiano Varagnolo. "Side-scan sonar based landmark detection for underwater vehicles." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801447.

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Marty, Baptiste, Bertrand Chemisky, and Didier Charlot. "Inertial Sidescan Sonar: Expanding Side Scan Sonar Processing by Leveraging Inertial Navigation Systems." In OCEANS 2024 - Halifax. IEEE, 2024. http://dx.doi.org/10.1109/oceans55160.2024.10754250.

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Sethuraman, Advaith V., Philip Baldoni, Katherine A. Skinner, and James McMahon. "Learning Which Side to Scan: Multi-View Informed Active Perception with Side Scan Sonar for Autonomous Underwater Vehicles." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611077.

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Bouwman, Fleur, David W. Ecclestone, Alexander L. Gabriëlse, and Alexander M. van Oers. "Synthetic side-scan sonar data for detecting mine-like objects." In Artificial Intelligence for Security and Defence Applications II, edited by Henri Bouma, Yitzhak Yitzhaky, Radhakrishna Prabhu, and Hugo J. Kuijf. SPIE, 2024. http://dx.doi.org/10.1117/12.3031150.

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Yang, Yunxiang, Yi Wei, Jing Guo, et al. "Recognition of side-scan sonar images under long-tail distribution." In 2024 International Conference on Computer Vision and Image Processing, edited by Xin Xu and Zhenghao Shi. SPIE, 2025. https://doi.org/10.1117/12.3058318.

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Okamoto, Riku, Masayoshi Ozawa, Huka Masai, et al. "Improved Identification Evaluation of Side-Scan Sonar for Blue Carbon." In 2025 IEEE Underwater Technology (UT). IEEE, 2025. https://doi.org/10.1109/ut61067.2025.10947278.

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Wang, Wenwu, Binbin Cheng, and Yao Chen. "Object detection in side scan sonar." In Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), edited by Zhiguo Cao, Jayaram K. Udupa, and Henri Maître. SPIE, 2015. http://dx.doi.org/10.1117/12.2205394.

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Coffelt, Jeremy Paul, and Jesper Haahr Christensen. "Anomaly Detection in Side-Scan Sonar." In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9705947.

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Reports on the topic "Side-scan sonar"

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Clausner, James E., and Joan Pope. Side-Scan Sonar Applications for Evaluating Coastal Structures. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203358.

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Zevenhuizen, J., and S. Solomon. Side scan sonar data interpretation, 1991, Beaufort Sea coastal survey. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/195127.

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Hart, B. S. Side-scan sonar observations of Point Grey dump site, Strait of Georgia, British Columbia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1992. http://dx.doi.org/10.4095/132787.

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D'Spain, Gerald L., and C. D. Chadwell. DURIP: Side Scan Sonar and Inertial Navigation System for AUV-Based Ocean Bottom/Sub-Bottom Mapping for Object Search/Identification. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada501314.

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Hart, B. S., J. V. Barrie, R. G. Currie, J. L. Luternauer, D. B. Prior, and R. D. Macdonald. High Resolution Seismic and Side - Scan Sonar Mapping of the Fraser Delta Front and Adjacent Strait of Georgia, British Columbia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/132624.

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D'Spain, Gerald L., and C. D. Chadwell. DURIP: Side Scan Sonar and Inertial Navigation System for AUV-Based Ocean Bottom/Sub-Bottom Mapping for Object Search/Identification. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada572723.

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Spiess, Volkhard, and Tilmann Schwenk. University Bremen Student Training Cruises: Advanced Marine Geophysical Survey Project, Seegeophysikalische Geländeübung Marine Geophysical Field Exercise, Cruise No. AL581/Leg1+2+3, 12.09.2022 – 26.09.2022, Kiel (Germany) – Kiel (Germany), GeophysPracUniBremen. University Bremen, 2025. https://doi.org/10.3289/cr_al581.

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The Advanced Marine Geophysical Survey Cruise AL581 took an international group of 10 scientists from the University of Bremen, including six Msc Marine Geosciences students, in September 2022 on the RV ALKOR to the German Baltic Sea, between Kiel and the eastern side of Rügen Using a variety of geophysical methods, the seafloor was imaged for training purposes in order to analyze geological structures and features, but also to detect anthropogenic influences, like submarine cables, pipelines, construction sites, traces of fishing and possible ammunition remnants from past wars. Students were
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Cumming, E. H., and G. V. Sonnichsen. White Rose repetitive seafloor mapping: a correlation of side scan sonar data and a swath bathmetry image from the Grand Banks, Newfoundland. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/211270.

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Tom, Joe, Marcelo Garcia, and Haode Wang. Review of Methodologies to Assess Bridge Safety During and After Floods. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-008.

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This report summarizes a review of technologies used to monitor bridge scour with an emphasis on techniques appropriate for testing during and immediately after design flood conditions. The goal of this study is to identify potential technologies and strategies for Illinois Department of Transportation that may be used to enhance the reliability of bridge safety monitoring during floods from local to state levels. The research team conducted a literature review of technologies that have been explored by state departments of transportation (DOTs) and national agencies as well as state-of-the-ar
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High-resolution hydroacoustic datasets of the Polish Southern Baltic Sea (2022–2023). Gdynia Maritime University, 2025. https://doi.org/10.26408/southern-baltic-hydroacoustic-datasets.

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This dataset comprises high-resolution bathymetric grids and side-scan sonar mosaics of the seafloor, covering over 7,300 km² of Polish marine areas in the southern Baltic Sea. The data were acquired between 2022 and 2023 as part of a national initiative to map benthic habitats using advanced multibeam echosounder (MBES) and side-scan sonar (SSS) technologies, in accordance with IHO S-44 Order 1a standards. The resulting products include bathymetric grids at 50 × 50 cm resolution and sonar mosaics at 20 × 20 cm resolution, with at least 95% data completeness per raster cell. Rigorous quality c
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