Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Recent Advances in Underwater and Terrestrial Remote Sensing

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: 1 June 2024 | Viewed by 10070

Share This Special Issue

Special Issue Editors

Dr. Qiang Ren

E-Mail Website
Guest Editor

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Interests: electromagnetic compatibility in integrated circuits

Prof. Dr. Mingyao Xia

E-Mail Website
Guest Editor

School of Electronics, Peking University, Beijing 100871, China
Interests: electromagnetic field theory and numerical methods; electromagnetic scattering and imaging; microwave components and antennas
Special Issues, Collections and Topics in MDPI journals

Dr. Jiefu Chen

E-Mail Website
Guest Editor

Department of Electrical & Computer Engineering, University of Houston, Houston, TX 77204, USA
Interests: deep learning; sensor

Dr. Yuanguo Zhou

E-Mail Website
Guest Editor

Department of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Interests: computational electromagnetics; electromagnetic scattering and imaging; inverse problems; ocean remote sensing

Dr. Danilo Orlando

E-Mail Website
Guest Editor

Department of Engineering, University of Niccolo Cusano, Via Don Carlo Gnocchi 3, 00166 Rome, Italy
Interests: the field of statistical signal processing with applications to radar and sonar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, due to rapid advances in microelectronics and computer technology, significant progress has been made in the development of remote sensing theory, technology, sensing equipment, carrier platforms and other related technologies, overcoming some of the problems regarding remote sensing on land and underwater. The purpose of this Special Issue is to report on advanced theoretical and experimental progress of remote sensing technology in terrestrial and underwater applications. It will focus on compiling a balanced collection of papers on the latest advances in this field, including research advances in geophysical exploration, detection, identification, signal processing, sensors and hardware platforms. Submissions are hereby invited for original research papers, reviews, letters and communications covering all aspects of terrestrial and underwater remote sensing processes.

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

Subsurface/ocean exploration;
Ocean surface and target;
Magnetic anomaly detection;
Underwater vehicle detection/location;
Navigation and positioning method;
Signal processing;
Application of acoustic remote sensing/sonar technology;
Acoustic/Electromagnetic propagation;
Machine learning for remote sensing;
Electromagnetic Remote Sensing;
Magnetic signal measurement;
Applications of magnetic sensors.

Dr. Qiang Ren
Prof. Dr. Mingyao Xia
Dr. Jiefu Chen
Dr. Yuanguo Zhou
Dr. Danilo Orlando
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

terrestrial/underwater remote sensing
sonar
geophysical
acoustic remote sensing
ocean target

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

20 pages, 24596 KiB

Open AccessArticle

UAV Time-Domain Electromagnetic System and a Workflow for Subsurface Targets Detection

by Kang Xing, Shiyan Li, Zhijie Qu, Miaomiao Gao, Yuan Gao and Xiaojuan Zhang

Remote Sens. 2024, 16(2), 330; https://doi.org/10.3390/rs16020330 - 13 Jan 2024

Viewed by 887

Abstract

The time-domain electromagnetic (TDEM) method is acknowledged for its simplicity in setup and non-intrusive detection capabilities, particularly within shallow subsurface detection methodologies. However, extant TDEM systems encounter constraints when detecting intricate topographies and hazardous zones. The rapid evolution in unmanned aerial vehicle (UAV) technology has engendered the inception of UAV-based time-domain electromagnetic systems, thereby augmenting detection efficiency while mitigating potential risks associated with human casualties. This study introduces the UAV-TDEM system designed explicitly for discerning shallow subsurface targets. The system comprises a UAV platform, a host system, and sensors that capture the electromagnetic response of the area while concurrently recording real-time positional data. This study also proposes a processing technique rooted in robust local mean decomposition (RLMD) and approximate entropy (ApEn) methodology to address noise within the original data. Initially, the RLMD decomposes the original data to extract residuals alongside multiple product functions (PFs). Subsequently, the residual is combined with various PFs to yield several cumulative sums, wherein the approximate entropy of these cumulative sums is computed, and the resulting output signals are filtered using a predetermined threshold. Ultimately, the YOLOv8 (You Only Look Once version 8) network is employed to extract anomalous regions. The proposed denoising method can process data within one second, and the trained YOLOv8 network achieves an accuracy rate of 99.0% in the test set. Empirical validation through multiple flight tests substantiates the efficiency of UAV-TDEM in detecting targets situated up to 1 m below the surface. Both simulated and measured data corroborate the proposed workflow’s effectiveness in mitigating noise and identifying targets. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Figure 1

28 pages, 13316 KiB

Open AccessArticle

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

by Mu Ding, Yatong Zhou and Yue Chi

Remote Sens. 2024, 16(2), 305; https://doi.org/10.3390/rs16020305 - 11 Jan 2024

Cited by 1 | Viewed by 568

Abstract

In light of the challenging conditions of exploration environments coupled with escalating exploration expenses, seismic data acquisition frequently entails the capturing of signals entangled amidst diverse noise interferences and instances of data loss. The unprocessed state of these seismic signals significantly jeopardizes the interpretative phase. Evidently, the integration of attention mechanisms and the utilization of generative adversarial networks (GANs) have emerged as prominent techniques within signal processing owing to their adeptness in discerning intricate global dependencies. Our research introduces a pioneering approach for reconstructing and denoising seismic signals, amalgamating the principles of self-attention and generative adversarial networks—hereafter referred to as SAGAN. Notably, the incorporation of the self-attention mechanism into the GAN framework facilitates an enhanced capacity for both the generator and discriminator to emulate meaningful spatial interactions. Subsequently, leveraging the feature map generated by the self-attention mechanism within the GAN structure enables the interpolation and denoising of seismic signals. Rigorous experimentation substantiates the efficacy of SAGAN in simultaneous signal interpolation and denoising. Initially, we benchmarked SAGAN against prominent methods such as UNet, CNN, and Wavelet for the concurrent interpolation and denoising of two-dimensional seismic signals manifesting varying levels of damage. Subsequently, this methodology was extended to encompass three-dimensional seismic data. Notably, performance metrics reveal SAGAN’s superiority over comparative methods. Specifically, the quantitative tables exhibit SAGAN’s pronounced advantage, with a 3.46% increase in PSNR value over UNet and an impressive 11.90% surge compared to Wavelet. Moreover, the RMSE values affirm SAGAN’s robust performance, showcasing an 11.54% reduction in comparison to UNet and an impressive 29.27% decrement relative to Wavelet, hence unequivocally establishing the SAGAN method as a preeminent choice for seismic signal recovery. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Graphical abstract

23 pages, 5752 KiB

Open AccessArticle

Comparative Study of 2D Lattice Boltzmann Models for Simulating Seismic Waves

by Muming Xia, Hui Zhou, Chuntao Jiang, Jinming Cui, Yong Zeng and Hanming Chen

Remote Sens. 2024, 16(2), 285; https://doi.org/10.3390/rs16020285 - 10 Jan 2024

Viewed by 636

Abstract

The simulation of seismic wavefields holds paramount significance in understanding subsurface structures and seismic events. The lattice Boltzmann method (LBM) provides a computational framework adept at capturing detailed wave interactions, offering a new approach to improve seismic wavefield simulations. Our study involves a novel comparative analysis of wavefields using different lattice Boltzmann models, focusing on how relaxation times, discrete velocity models, and collision operators affect simulation accuracy and efficiency. We explore the impacts of distinct relaxation times and evaluate their effects on wave propagation speed and fidelity. By incorporating four discrete velocity models of LBM, we innovatively investigate the trade-off between spatial resolution and computational complexity. Additionally, we delve into the implications of employing three collision operators—single relaxation time (SRT), two relaxation times (TRT), and multiple relaxation times (MRT). By comparing their accuracy and stability, we provide insights into selecting the most suitable collision operator for capturing complex wave interactions. Our research provides a comprehensive framework to optimize the LBM parameters, enhancing both accuracy and efficiency in seismic wave simulations, and offers valuable insights to benefit wave simulation across diverse disciplines. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Graphical abstract

23 pages, 29774 KiB

Open AccessArticle

Adversarial Attacks in Underwater Acoustic Target Recognition with Deep Learning Models

by Sheng Feng, Xiaoqian Zhu, Shuqing Ma and Qiang Lan

Remote Sens. 2023, 15(22), 5386; https://doi.org/10.3390/rs15225386 - 16 Nov 2023

Viewed by 995

Abstract

Deep learning models can produce unstable results by introducing imperceptible perturbations that are difficult for humans to recognize. This can have a significant impact on the accuracy and security of deep learning applications due to their poorly understood interpretability. As a field critical to security research, this problem clearly exists in underwater acoustic target recognition for ocean sensing. To address this issue, this article investigates the reliability of state-of-the-art deep learning models by exploring adversarial attack methods that add small, exquisite perturbations on acoustic Mel-spectrograms to generate adversarial spectrograms. Experimental results based on real-world datasets reveal that these models can be forced to learn unexpected features when subjected to adversarial spectrograms, resulting in significant accuracy drops. Specifically, when employing the iterative attack method, the overall accuracy of all models experiences a significant decrease of approximately 70% for two datasets under stronger perturbations. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Graphical abstract

15 pages, 4162 KiB

Open AccessCommunication

GPU-Accelerated Monte Carlo Simulation for a Single-Photon Underwater Lidar

by Yupeng Liao, Mingjia Shangguan, Zhifeng Yang, Zaifa Lin, Yuanlun Wang and Sihui Li

Remote Sens. 2023, 15(21), 5245; https://doi.org/10.3390/rs15215245 - 05 Nov 2023

Viewed by 1168

Abstract

The Monte Carlo (MC) simulation, due to its ability to accurately simulate the backscattered signal of lidar, plays a crucial role in the design, optimization, and interpretation of the backscattered signal in lidar systems. Despite the development of several MC models for lidars, a suitable MC simulation model for underwater single-photon lidar, which is a vital ocean remote sensing technique utilized in underwater scientific investigations, obstacle avoidance for underwater platforms, and deep-sea environmental exploration, is still lacking. There are two main challenges in underwater lidar simulation. Firstly, the simulation results are significantly affected by near-field abnormal signals. Secondly, the simulation process is time-consuming due to the requirement of a high number of random processes to obtain reliable results. To address these issues, an algorithm is proposed to minimize the impacts of abnormal simulation signals. Additionally, a graphics processing unit (GPU)-accelerated semi-analytic MC simulation with a compute unified device architecture is proposed. The performance of the GPU-based program was validated using 10⁹ photons and compared to a central processing unit (CPU)-based program. The GPU-based program achieved up to 68 times higher efficiency and a maximum relative deviation of less than 1.5%. Subsequently, the MC model was employed to simulate the backscattered signal in inhomogeneous water using the Henyey–Greenstein phase functions. By utilizing the look-up table method, simulations of backscattered signals were achieved using different scattering phase functions. Finally, a comparison between the simulation results and measurements derived from an underwater single-photon lidar demonstrated the reliability and robustness of our GPU-based MC simulation model. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Figure 1

16 pages, 5220 KiB

Open AccessCommunication

Underwater Single-Photon Lidar Equipped with High-Sampling-Rate Multi-Channel Data Acquisition System

by Zaifa Lin, Mingjia Shangguan, Fuqing Cao, Zhifeng Yang, Ying Qiu and Zhenwu Weng

Remote Sens. 2023, 15(21), 5216; https://doi.org/10.3390/rs15215216 - 02 Nov 2023

Cited by 1 | Viewed by 1209

Abstract

Lidar has emerged as an important technology for the high-precision three-dimensional remote sensing of the ocean. While oceanic lidar has been widely deployed on various platforms, its underwater deployment is relatively limited, despite its significance in deep-sea exploration and obstacle avoidance for underwater platforms. Underwater lidar systems must meet stringent requirements for high performance, miniaturization, and high integration. Single-photon lidar, by elevating the detection sensitivity to the single-photon level, enables high-performance detection under the condition of a low-pulse-energy laser and a small-aperture telescope, making it a stronger candidate for underwater lidar applications. However, this imposes demanding requirements for the data acquisition system utilized in single-photon lidar systems. In this work, a self-developed multi-channel acquisition system (MCAS) with a high-resolution and real-time histogram statistics capability was developed. By utilizing field-programmable gate array (FPGA) technology, a method that combines coarse counters with multi-phase clock interpolation achieved an impressive resolution of 0.5 ns and enabled a time of flight duration of 1.5 μs. To address counting instability, a dual-counter structure was adopted in the coarse counter, and real-time histogram statistics were achieved in the data acquisition system through a state machine. Furthermore, the non-uniform phase shift of the clock was analyzed, and a correction algorithm based on code density statistics was proposed to mitigate the periodic modulation of the backscattered signal, with the effectiveness of the algorithm demonstrated through experimental results. The robustness and stability of the MCAS were validated through an underwater experiment. Ultimately, the development of this compact acquisition system enables the implementation of underwater single-photon lidar systems, which will play a crucial role in underwater target imaging, obstacle avoidance in underwater platforms, and deep-sea marine environment monitoring. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Figure 1

17 pages, 3848 KiB

Open AccessArticle

A Multi-Objective Geoacoustic Inversion of Modal-Dispersion and Waveform Envelope Data Based on Wasserstein Metric

by Jiaqi Ding, Xiaofeng Zhao, Pinglv Yang and Yapeng Fu

Remote Sens. 2023, 15(19), 4893; https://doi.org/10.3390/rs15194893 - 09 Oct 2023

Viewed by 806

Abstract

The inversion of acoustic field data to estimate geoacoustic parameters has been a prominent research focus in the field of underwater acoustics for several decades. Modal-dispersion curves have been used to inverse seabed sound speed and density profiles, but such techniques do not account for attenuation inversion. In this study, a new approach where modal-dispersion and waveform envelope data are simultaneously inversed under a multi-objective framework is proposed. The inversion is performed using the Multi-Objective Bayesian Optimization (MOBO) method. The posterior probability densities (PPD) of the estimation results are obtained by resampling from the exploited state space using the Gibbs Sampler. In this study, the implemented MOBO approach is compared with individual inversions both from modal-dispersion curves and the waveform data. In addition, the effective use of the Wasserstein metric from optimal transport theory is explored. Then the MOBO performance is tested against two different cost functions based on the L2 norm and the Wasserstein metric, respectively. Numerical experiments are employed to evaluate the effect of different cost functions on inversion performance. It is found that the MOBO approach may have more profound advantages when applied to Wasserstein metrics. Results obtained from our study reveal that the MOBO approach exhibits reduced uncertainty in the inverse results when compared to individual inversion methods, such as modal-dispersion inversion or waveform inversion. However, it is important to note that this enhanced uncertainty reduction comes at the cost of sacrificing accuracy in certain parameters other than the sediment sound speed and attenuation. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Figure 1

Other

Jump to: Research

16 pages, 8323 KiB

Open AccessTechnical Note

Prediction of Water Temperature Based on Graph Neural Network in a Small-Scale Observation via Coastal Acoustic Tomography

by Pan Xu, Shijie Xu, Kequan Shi, Mingyu Ou, Hongna Zhu, Guojun Xu, Dongbao Gao, Guangming Li and Yun Zhao

Remote Sens. 2024, 16(4), 646; https://doi.org/10.3390/rs16040646 - 09 Feb 2024

Viewed by 538

Abstract

Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity by inversely estimating the acoustic ray speed during its traversal through the aquatic medium. Concurrently, analyzing the speed of different acoustic waves in their round-trip propagation enables the inverse estimation of dynamic hydrographic features, including flow velocity and directional attributes. An accurate forecasting of inversion answers in CAT rapidly contributes to a comprehensive analysis of the evolving ocean environment and its inherent characteristics. Graph neural network (GNN) is a new network architecture with strong spatial modeling and extraordinary generalization. We proposed a novel method: employing GraphSAGE to predict inversion answers in OAT, using experimental datasets collected at the Huangcai Reservoir for prediction. The results show an average error 0.01% for sound speed prediction and 0.29% for temperature predictions along each station pairwise. This adequately fulfills the real-time and exigent requirements for practical deployment. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Figure 1

15 pages, 4148 KiB

Open AccessTechnical Note

Adaptive Basis Function Method for the Detection of an Undersurface Magnetic Anomaly Target

by Xingen Liu, Zifan Yuan, Changping Du, Xiang Peng, Hong Guo and Mingyao Xia

Remote Sens. 2024, 16(2), 363; https://doi.org/10.3390/rs16020363 - 16 Jan 2024

Viewed by 638

Abstract

The orthogonal basis functions (OBFs) method is a prevailing choice for the detection of undersurface magnetic anomaly targets. However, it requires the detecting platform or target to move uniformly along a straight path. To circumvent the restrictions, a new adaptive basis functions (ABFs) approach is proposed in this article. It permits the detection platform to search for a possible target at different speeds along any course. The ABFs are constructed using the real-time data of the onboard triaxial fluxgate, GPS module, and attitude gyro. Based on the pseudo-energy of an apparent target signal, the constant false alarm rate (CFAR) method is employed to judge whether a target is present. Moreover, by defining the pixel as a relative possibility for a target at a geographic location, a magnetic anomaly target imaging scheme is introduced by displaying the pixels onto the searching area. On-site experimental data are utilized to demonstrate the proposed approach. Compared with the traditional OBFs method, the present ABFs approach can substantially improve the detection possibility and reduce false alarms. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Figure 1

17 pages, 5530 KiB

Open AccessTechnical Note

Attitude-Independent Route Tracking for Subsea Power Cables Using a Scalar Magnetometer under High Sea Conditions

by Guozhu Li, Xuxing Geng, Shangqing Liang, Yuanpeng Chen, Guangming Huang, Gaoxiang Li, Xueting Zhang and Guoqing Yang

Remote Sens. 2024, 16(2), 226; https://doi.org/10.3390/rs16020226 - 06 Jan 2024

Viewed by 723

Abstract

To overcome the shortcoming wherein the accuracy of subsea cable detection can be affected by the determination of the bias vector, scale factors, and non-orthogonality corrections of the vector magnetometer, a real-time attitude-independent route tracking method for subsea power cables is investigated theoretically and experimentally by means of scalar magnetic field checking. The measurement of the magnetic field B_c produced by the current in a cable is made immune to the influence of the platform attitude by extracting the component of B_c along the geomagnetic field using a high-bandwidth self-oscillating optically pumped magnetometer. The self-oscillating frequency is proved to be independent of the attitude of the magnetometer with the theoretical model. Experiments are carried out to test the attitude-independent performance, and the effectiveness of route tracking is verified by the results of the sea experiment. The proposed method will effectively improve the ability to locate subsea cables under high sea conditions. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Figure 1

17 pages, 3915 KiB

Open AccessTechnical Note

Eigenvector Constraint-Based Method for Eliminating Dead Zone in Magnetic Target Localization

by Wangwang Tang, Guangming Huang, Gaoxiang Li and Guoqing Yang

Remote Sens. 2023, 15(20), 4959; https://doi.org/10.3390/rs15204959 - 14 Oct 2023

Viewed by 816

Abstract

Magnetic target localization using the magnetic gradient tensor (MGT) plays a significant role in underwater localization. However, this method inherently has a localization dead zone, which presents challenges for real-world applications. This paper delves into the root cause of this dead zone, identifying the non-invertibility of the MGT when the magnetic moment vector is orthogonal to the position vector from the target to the observation point. To tackle this issue, a method based on the eigenvector constraints is proposed. By constructing an objective function with eigenvector constraints and leveraging the property that its gradient at the observation point is zero, we derive an equivalent expression for the inverse of MGT that always holds and further develop a dead-zone-free localization method. To validate the robustness and efficacy of the proposed localization method, a comparative analysis with other methods is conducted. Simulation results in a 10 m × 10 m area under Gaussian noise demonstrate the proposed method’s capability to eliminate the dead zone and achieve an average localization error of 0.032 m. Experimental results further demonstrate that the proposed method eliminates the localization dead zone and exhibits greater robustness than the dominant method in the normal region. In summation, this paper provides an effective method for eliminating localization dead zone, offering a more stable and reliable method for magnetic target localization in practice. Full article

(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)

► Show Figures

Journal Menu

Journal Browser

Recent Advances in Underwater and Terrestrial Remote Sensing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (11 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI