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Remote Sensing
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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: 30 April 2024 | Viewed by 1157

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

Related Special Issue

Published Papers (2 papers)

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Research

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
Viewed by 363
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
Viewed by 524
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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