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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 36101

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Prof. Dr. Bogusław Cyganek

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Faculty of Computer Science, Electronics and Telecommunication, Department of Electronics, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: computer vision; machine learning; artificial intelligence; software development in C++ and Python
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ocean, covering over 70% of our planet and reaching depths of nearly 11,000 meters, is the last largely unexplored environment on Earth. Since a vast majority of electromagnetic waves, including light, penetrate the ocean depths only in a very limited degree, sensing and exploring the immense ocean environment presents exceptional challenges. Despite great advances in the sensing technologies, there are still serious challenges in their successful applications in various domains of marine recognition. Hence, development of novel methods, joining modern sensing techniques operating with diverse signal types, is of primary interest for the research and marine industry.

The purpose of this Special Issue is to provide a platform for information exchange and knowledge sharing, as well as to gather the latest research achievements, in the broad subject of sensor applications on marine recognition viewed from both the science and industry corners.

Original submissions from all areas related to sensor applications on marine recognition are welcome. Topics of interest include but are not limited to the following ones.

Keywords:

Marine sensors technologies
Marine recognition for biology
Underwater search and exploration
Sensing for autonomous underwater drones
Underwater drones navigation
Deep learning and AI methods in marine recognition
Deep-sea multispectral and hyperspectral sensing
Computer vision and image processing methods for marine recognition
Automated detection, classification, and segmentation of marine objects
Underwater image and video restoration methods
Tensor based methods for multidimensional signal processing
Deep-sea optical imaging technologies
Acoustic imaging in marine recognition
Recognition from sidescan and multibeam sonars
Restoration and filtering of the sidescan and multibeam sonar signals
Seismic monitoring and imaging
Multichannel seismic technologies
Data management, annotations, access, visualization, and sharing

Prof. Dr. Bogusław Cyganek

Guest Editor

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Published Papers (7 papers)

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Research

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16 pages, 2196 KiB

Open AccessArticle

ALMI—A Generic Active Learning System for Computational Object Classification in Marine Observation Images

by Torben Möller and Tim W. Nattkemper

Sensors 2021, 21(4), 1134; https://doi.org/10.3390/s21041134 - 06 Feb 2021

Cited by 3 | Viewed by 1846

Abstract

In recent years, an increasing number of cabled Fixed Underwater Observatories (FUOs) have been deployed, many of them equipped with digital cameras recording high-resolution digital image time series for a given period. The manual extraction of quantitative information from these data regarding resident species is necessary to link the image time series information to data from other sensors but requires computational support to overcome the bottleneck problem in manual analysis. As a priori knowledge about the objects of interest in the images is almost never available, computational methods are required that are not dependent on the posterior availability of a large training data set of annotated images. In this paper, we propose a new strategy for collecting and using training data for machine learning-based observatory image interpretation much more efficiently. The method combines the training efficiency of a special active learning procedure with the advantages of deep learning feature representations. The method is tested on two highly disparate data sets. In our experiments, we can show that the proposed method ALMI achieves on one data set a classification accuracy A > 90% with less than N = 258 data samples and A > 80% after N = 150 iterations, i.e., training samples, on the other data set outperforming the reference method regarding accuracy and training data required. Full article

(This article belongs to the Special Issue Sensor Applications on Marine Recognition)

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14 pages, 2797 KiB

Open AccessArticle

Compact Quantum Magnetometer System on an Agile Underwater Glider

by Brian R. Page, Reeve Lambert, Nina Mahmoudian, David H. Newby, Elizabeth L. Foley and Thomas W. Kornack

Sensors 2021, 21(4), 1092; https://doi.org/10.3390/s21041092 - 05 Feb 2021

Cited by 12 | Viewed by 5455

Abstract

This paper presents results from the integration of a compact quantum magnetometer system and an agile underwater glider for magnetic survey. A highly maneuverable underwater glider, ROUGHIE, was customized to carry an increased payload and reduce the vehicle’s magnetic signature. A sensor suite composed of a vector and scalar magnetometer was mounted in an external boom at the rear of the vehicle. The combined system was deployed in a constrained pool environment to detect seeded magnetic targets and create a magnetic map of the test area. Presented is a systematic magnetic disturbance reduction process, test procedure for anomaly mapping, and results from constrained operation featuring underwater motion capture system for ground truth localization. Validation in the noisy and constrained pool environment creates a trajectory towards affordable littoral magnetic anomaly mapping infrastructure. Such a marine sensor technology will be capable of extended operation in challenging areas while providing high-resolution, timely magnetic data to operators for automated detection and classification of marine objects. Full article

(This article belongs to the Special Issue Sensor Applications on Marine Recognition)

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26 pages, 2490 KiB

Open AccessArticle

MorphoCluster: Efficient Annotation of Plankton Images by Clustering

by Simon-Martin Schröder, Rainer Kiko and Reinhard Koch

Sensors 2020, 20(11), 3060; https://doi.org/10.3390/s20113060 - 28 May 2020

Cited by 29 | Viewed by 4992

Abstract

In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection. Full article

(This article belongs to the Special Issue Sensor Applications on Marine Recognition)

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16 pages, 9165 KiB

Open AccessArticle

Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets

by Alberto Testolin and Roee Diamant

Sensors 2020, 20(10), 2945; https://doi.org/10.3390/s20102945 - 22 May 2020

Cited by 22 | Viewed by 3228

Abstract

Accurate detection and tracking of moving targets in underwater environments pose significant challenges, because noise in acoustic measurements (e.g., SONAR) makes the signal highly stochastic. In continuous marine monitoring a further challenge is related to the computational complexity of the signal processing pipeline—due to energy constraints, in off-shore monitoring platforms algorithms should operate in real time with limited power consumption. In this paper, we present an innovative method that allows to accurately detect and track underwater moving targets from the reflections of an active acoustic emitter. Our system is based on a computationally- and energy-efficient pre-processing stage carried out using a deep convolutional denoising autoencoder (CDA), whose output is then fed to a probabilistic tracking method based on the Viterbi algorithm. The CDA is trained on a large database of more than 20,000 reflection patterns collected during 50 designated sea experiments. System performance is then evaluated on a controlled dataset, for which ground truth information is known, as well as on recordings collected during different sea experiments. Results show that, compared to the benchmark, our method achieves a favorable trade-off between detection and false alarm rate, as well as improved tracking accuracy. Full article

(This article belongs to the Special Issue Sensor Applications on Marine Recognition)

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15 pages, 3617 KiB

Open AccessArticle

Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

by Jian He, Yongfei Guo and Hangfei Yuan

Sensors 2020, 20(9), 2536; https://doi.org/10.3390/s20092536 - 29 Apr 2020

Cited by 12 | Viewed by 2073

Abstract

Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds. Full article

(This article belongs to the Special Issue Sensor Applications on Marine Recognition)

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Review

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17 pages, 4043 KiB

Open AccessReview

Direction-of-Arrival Estimation Methods in Interferometric Echo Sounding

by Piotr Grall, Iwona Kochanska and Jacek Marszal

Sensors 2020, 20(12), 3556; https://doi.org/10.3390/s20123556 - 23 Jun 2020

Cited by 10 | Viewed by 5285

Abstract

Nowadays, there are two leading sea sounding technologies: the multibeam echo sounder and the multiphase echo sounder (also known as phase-difference side scan sonar or bathymetric side scan sonar). Both solutions have their advantages and disadvantages, and they can be perceived as complementary to each other. The article reviews the development of interferometric echo sounding array configurations and the various methods applied to determine the direction-of-arrival. “Interferometric echo sounder” is a broad term, applied to various devices that primarily utilize phase difference measurements to estimate the direction-of-arrival. The article focuses on modifications to the interferometric sonar array that have led to the state-of-the-art multiphase echo sounder. The main algorithms for classical and modern interferometric echo sounder direction-of-arrival estimation are also outlined. The accuracy of direction-of-arrival estimation methods is dependent on the configuration of the array and external and internal noise sources. The main sources of errors, which influence the accuracy of the phase difference measurements, are also briefly characterized. The article ends with a review of the current research into improvements in the accuracy of interferometric echo sounding and the application of the principle of interferometric in other devices. Full article

(This article belongs to the Special Issue Sensor Applications on Marine Recognition)

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39 pages, 3130 KiB

Open AccessEditor’s ChoiceReview

Sensors to Increase the Security of Underwater Communication Cables: A Review of Underwater Monitoring Sensors

by Dimitrios Eleftherakis and Raul Vicen-Bueno

Sensors 2020, 20(3), 737; https://doi.org/10.3390/s20030737 - 29 Jan 2020

Cited by 23 | Viewed by 12151

Abstract

Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last few years, several reports raised issues about possible future malicious attacks on such cables. The main objective of this operational research and analysis (ORA) paper is to present an overview of different commercial and already available marine sensor technologies (acoustic, optic, magnetic and oceanographic) that could be used for autonomous monitoring of the underwater cable environment. These sensors could be mounted on different autonomous platforms, such as unmanned surface vehicles (USVs) or autonomous underwater vehicles (AUVs). This paper analyses a multi-threat sabotage scenario where surveying a transatlantic cable of 13,000 km, (reaching water depths up to 4000 m) is necessary. The potential underwater threats identified for such a scenario are: divers, anchors, fishing trawls, submarines, remotely operated vehicles (ROVs) and AUVs. The paper discusses the capabilities of the identified sensors to detect such identified threats for the scenario under study. It also presents ideas on the construction of periodic and permanent surveillance networks. Research study and results are focused on providing useful information to decision-makers in charge of designing surveillance capabilities to secure underwater communication cables. Full article

(This article belongs to the Special Issue Sensor Applications on Marine Recognition)

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