Underwater Acoustic Imaging and Image Processing Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (25 March 2022) | Viewed by 2682

Special Issue Editor

Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
Interests: underwater imaging system; underwater acoustic signal processing; underwater target classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Underwater acoustic imaging is one of the most important technologies to obtain underwater information. Acoustic imaging has become the core technology of seafloor topography, underwater shipwreck salvage, underwater unexploded ordnance detection, underwater archaeology, search for pipelines and other human-made objects, marine geological surveys, and so on. In recent years, high-dimensional, high-resolution, and high-efficiency underwater acoustic imaging systems have become more easily implemented due to the evolution of imaging algorithms and the continuous improvement and innovation of imaging components, such as sound transducers and signal receivers. On the other hand, the great progress which has been achieved in the fields of digital signal processing, computer vision, parallel calculation, etc. has enabled larger-scale, faster, and more efficient information computing. Advances in these two fields have contributed to the wide use of underwater acoustic imaging equipment. As one of the hotspots in the research field of underwater acoustic imaging, image processing technology makes use of the advances of neural networks, pattern recognition, machine learning, computational reconstruction, and other advanced technology. A lot of progress has been made in image reconstruction, image stitching, classification, and recognition. To show the latest research progress, promote academic exchanges, and promote the development of related fields, we have set up a Special Issue of “Underwater Acoustic Imaging and Image Processing Technology” and invite research papers from experts and scholars. Potential topics include but are not limited to the following:

  • Design and development of acoustic imaging systems;
  • Applications of underwater acoustic imaging sonar;
  • Analysis and processing of underwater acoustic images;
  • Detection and classification in underwater acoustic images.

Dr. Jie Tian
Guest Editor

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Keywords

  • Side-scan sonar
  • Synthetic aperture sonar
  • Multibeam sonar
  • Sub-bottom profiling
  • Underwater acoustic tomography
  • Underwater 3D acoustic imaging
  • Image reconstruction
  • Image mosaic
  • Image processing
  • Object identification and classification
  • Moving target detection and tracking
  • Underwater security techniques

Published Papers (1 paper)

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Research

13 pages, 1891 KiB  
Article
Underwater Object Classification Method Based on Depthwise Separable Convolution Feature Fusion in Sonar Images
by Wenjing Gong, Jie Tian and Jiyuan Liu
Appl. Sci. 2022, 12(7), 3268; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073268 - 23 Mar 2022
Cited by 5 | Viewed by 1594
Abstract
In order to improve the accuracy of underwater object classification, according to the characteristics of sonar images, a classification method based on depthwise separable convolution feature fusion is proposed. Firstly, Markov segmentation is used to segment the highlight and shadow regions of the [...] Read more.
In order to improve the accuracy of underwater object classification, according to the characteristics of sonar images, a classification method based on depthwise separable convolution feature fusion is proposed. Firstly, Markov segmentation is used to segment the highlight and shadow regions of the object to avoid the loss of information caused by simultaneous segmentation. Secondly, depthwise separable convolution is used to learn the deep information of images for feature extraction, which produces less network computation. Thirdly, features of highlight and shadow regions are fused by the parallel network structure, and pyramid pooling is added to extract the multi-scale information. Finally, the full connection layers are used to achieve object classification through the Softmax function. Experiments are conducted on simulated and real data. Results show that the method proposed in this paper achieve superior performance compared with other models, and it also has certain flexibility. Full article
(This article belongs to the Special Issue Underwater Acoustic Imaging and Image Processing Technology)
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