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Remote Sensing of Target Detection in Marine Environment

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

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 81681

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Special Issue Editors

Dr. Ferdinando Nunziata

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Guest Editor

Dipartimento di Ingegneria, Università di Napoli Parthenope, 80133 Napoli, NA, Italy
Interests: synthetic aperture radar for sea observation; microwave radiometry; sea surface scattering; GNSS reflectometry
Special Issues, Collections and Topics in MDPI journals

Dr. Armando Marino

E-Mail Website
Guest Editor

The University of Stirling, Natural Sciences, Stirling FK9 4LA, UK
Interests: processing of stacks of polarimetric synthetic aperture radar (PolSAR) images for environmental applications with special focus on target detection (e.g. ship and iceberg); change detection (e.g. deforestation and erosion) and classification (e.g. agricultural crops)

Dr. Domenico Velotto

E-Mail Website
Guest Editor

Maritime Safety and Security Lab, Remote Sensing Technology Institute, German Aerospace Center (DLR), 28199 Bremen, Germany
Interests: synthetic aperture radar (SAR); Remote sensing; SAR polarimetry; signal processing; image processing, radar detection, machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The observation of targets at sea, such as ships or oil/gas rigs/platforms and wind turbines, is nowadays a key application in the field of global monitoring of environment and security. Synthetic aperture radar (SAR) imagery gives the possibility to overcome the limits of conventional techniques, e.g., Automatic Identification System (AIS), etc., allowing non-cooperative all-day ship surveillance, over wide regions and under almost all weather conditions. An increasing number of SAR satellites have become available since the early 1990s. This unprecedented development in SAR sensors requires the definition of new techniques and algorithms to detect marine targets, as well as in the assessment of existing methods. Hence, although there is a great deal of literature that concerns SAR methods to detect target at sea, there is still room for improvements to both models and methods.

The main purpose of this Special issue is to provide a reference of SAR methods to detect targets at sea, as well as to boost new methods and techniques. The topics of this Special Issue include, but are not limited to, the following subjects:

Target detection using moderate-to-high resolution SAR data
Target detection using spectral techniques
Target recognition
Backscattering analysis
Polarimetric models and methods to detect targets at sea
Target classification
Features extraction
Multi-resolution analysis
Ship wakes

Dr. Ferdinando Nunziata
Dr. Armando Marino
Dr. Domenico Velotto
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

SAR
Ocean backscattering
Metallic targets
Target recognition
Classification
RADAR polarimetry
Feature extraction
Ocean clutter

Published Papers (14 papers)

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Editorial

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3 pages, 165 KiB

Open AccessEditorial

Editorial for the Special Issue “Remote Sensing of Target Detection in Marine Environment”

by Ferdinando Nunziata, Armando Marino and Domenico Velotto

Remote Sens. 2019, 11(14), 1689; https://doi.org/10.3390/rs11141689 - 17 Jul 2019

Viewed by 2406

Abstract

Remote sensing is a powerful tool used to obtain an unprecedented amount of information about the ocean from a distance, usually from satellites or aircrafts [...] Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

Research

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36 pages, 29409 KiB

Open AccessArticle

Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection

by Sushil Kumar Joshi, Stefan V. Baumgartner, Andre B. C. da Silva and Gerhard Krieger

Remote Sens. 2019, 11(11), 1270; https://doi.org/10.3390/rs11111270 - 28 May 2019

Cited by 39 | Viewed by 5875

Abstract

Ship detection is an essential maritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detection methods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppler domain using suitable distribution functions. Detection in range-Doppler has the advantage that principally even small ships with a low radar cross section (RCS) can be detected if they are moving fast enough so that the ship signals are shifted to the exo-clutter region. In order to determine a robust threshold, the ocean statistics have to be described accurately. Bright target peaks in the background ocean data bias the statistics and lead to an erroneous threshold. Therefore, an automatic ocean training data extraction procedure is proposed in the paper. It includes (1) a novel target pre-detection module that removes the bright peaks from the data already in time domain, (2) clutter normalization in the Doppler domain using the remaining samples, (3) ocean statistics estimation and (4) threshold computation. Various sea clutter models are investigated and analyzed in the paper for finding the most suitable models for the RC data. The robustness and applicability of the proposed method is validated using real linearly and circularly acquired radar data from DLR’s (Deutsches Zentrum für Luft- und Raumfahrt) airborne F-SAR system. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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21 pages, 33279 KiB

Open AccessArticle

Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data

by Ramona Pelich, Marco Chini, Renaud Hostache, Patrick Matgen, Carlos Lopez-Martinez, Miguel Nuevo, Philippe Ries and Gerd Eiden

Remote Sens. 2019, 11(9), 1078; https://doi.org/10.3390/rs11091078 - 07 May 2019

Cited by 39 | Viewed by 5344

Abstract

This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to

40 %

for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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13 pages, 3003 KiB

Open AccessArticle

Maritime Over the Horizon Sensor Integration: HFSWR Data Fusion Algorithm

by Dejan Nikolic, Nikola Stojkovic, Zdravko Popovic, Nikola Tosic, Nikola Lekic, Zoran Stankovic and Nebojsa Doncov

Remote Sens. 2019, 11(7), 852; https://doi.org/10.3390/rs11070852 - 09 Apr 2019

Cited by 19 | Viewed by 4823

Abstract

In order to provide a constant and complete operational picture of the maritime situation in the Exclusive Economic Zone (EEZ) at over the horizon (OTH) distances, a network of high frequency surface-wave-radars (HFSWR) slowly becomes a necessity. Since each HFSWR in the network tracks all the targets it detects independently of other radars in the network, there will be situations where multiple tracks are formed for a single vessel. The algorithm proposed in this paper utilizes radar tracks obtained from individual HFSWRs which are already processed by the multi-target tracking algorithm at the single radar level, and fuses them into a unique data stream. In this way, the data obtained from multiple HFSWRs originating from the very same target are weighted and combined into a single track. Moreover, the weighting approach significantly reduces inaccuracy. The algorithm is designed, implemented, and tested in a real working environment. The testing environment is located in the Gulf of Guinea and includes a network of two HFSWRs. In order to validate the algorithm outputs, the position of the vessels was calculated by the algorithm and compared with the positions obtained from several coastal sites, with LAIS receivers and SAIS data provided by a SAIS provider. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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14 pages, 4073 KiB

Open AccessArticle

Changes in a Giant Iceberg Created from the Collapse of the Larsen C Ice Shelf, Antarctic Peninsula, Derived from Sentinel-1 and CryoSat-2 Data

by Hyangsun Han, Sungjae Lee, Jae-In Kim, Seung Hee Kim and Hyun-cheol Kim

Remote Sens. 2019, 11(4), 404; https://doi.org/10.3390/rs11040404 - 17 Feb 2019

Cited by 21 | Viewed by 5688

Abstract

The giant tabular iceberg A68 broke away from the Larsen C Ice Shelf, Antarctic Peninsula, in July 2017. The evolution of A68 would have been affected by both the Larsen C Ice Shelf, the surrounding sea ice, and the nearby shallow seafloor. In this study, we analyze the initial evolution of iceberg A68A—the largest originating from A68—in terms of changes in its area, drift speed, rotation, and freeboard using Sentinel-1 synthetic aperture radar (SAR) images and CryoSat-2 SAR/Interferometric Radar Altimeter observations. The area of iceberg A68A sharply decreased in mid-August 2017 and mid-May 2018 via large calving events. In September 2018, its surface area increased, possibly due to its longitudinal stretching by melting of surrounding sea ice. The decrease in the area of A68A was only 2% over 1.5 years. A68A was relatively stationary until mid-July 2018, while it was surrounded by the Larsen C Ice Shelf front and a high concentration of sea ice, and when its movement was interrupted by the shallow seabed. The iceberg passed through a bay-shaped region in front of the Larsen C Ice Shelf after July 2018, showing a nearly circular motion with higher speed and greater rotation. Drift was mainly inherited from its rotation, because it was still located near the Bawden Ice Rise and could not pass through by the shallow seabed. The freeboard of iceberg A68A decreased at an average rate of −0.80 ± 0.29 m/year during February–November 2018, which could have been due to basal melting by warm seawater in the Antarctic summer and increasing relative velocity of iceberg and ocean currents in the winter of that year. The freeboard of the iceberg measured using CryoSat-2 could represent the returned signal from the snow surface on the iceberg. Based on this, the average rate of thickness change was estimated at −12.89 ± 3.34 m/year during the study period considering an average rate of snow accumulation of 0.82 ± 0.06 m/year predicted by reanalysis data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The results of this study reveal the initial evolution mechanism of iceberg A68A, which cannot yet drift freely due to the surrounding terrain and sea ice. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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16 pages, 1420 KiB

Open AccessEditor’s ChoiceArticle

Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea

by Boris Snapir, Toby W. Waine and Lauren Biermann

Remote Sens. 2019, 11(3), 353; https://doi.org/10.3390/rs11030353 - 11 Feb 2019

Cited by 35 | Viewed by 6455

Abstract

Integration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel detection on satellite imagery, but research on vessel classification has mainly focused on bulk carriers, container ships, and oil tankers, using high-resolution commercial Synthetic Aperture Radar (SAR) imagery. Here, we present a method based on Random Forest (RF) to distinguish fishing and non-fishing vessels, and apply it to an area in the North Sea. The RF classifier takes as input the vessel’s length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the Automatic Identification System (AIS). The overall classification accuracy is 91%, but the precision for the fishing class is only 58% because of specific regions in the study area where activities of fishing and non-fishing vessels overlap. We then apply the classifier to a collection of vessel detections obtained by applying the Search for Unidentified Maritime Objects (SUMO) vessel detector to the 2017 Sentinel-1 SAR images of the North Sea. The trend in our monthly fishing-vessel count agrees with data from Global Fishing Watch on fishing-vessel presence. These initial results suggest that our approach could help monitor intensification or reduction of fishing activity, which is critical in the context of the global overfishing problem. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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24 pages, 7506 KiB

Open AccessArticle

Modeling the Amplitude Distribution of Radar Sea Clutter

by Sébastien Angelliaume, Luke Rosenberg and Matthew Ritchie

Remote Sens. 2019, 11(3), 319; https://doi.org/10.3390/rs11030319 - 06 Feb 2019

Cited by 35 | Viewed by 4795

Abstract

Ship detection in the maritime domain is best performed with radar due to its ability to surveil wide areas and operate in almost any weather condition or time of day. Many common detection schemes require an accurate model of the amplitude distribution of radar echoes backscattered by the ocean surface. This paper presents a review of select amplitude distributions from the literature and their ability to represent data from several different radar systems operating from 1 GHz to 10 GHz. These include the K distribution, arguably the most popular model from the literature as well as the Pareto, K+Rayleigh, and the trimodal discrete (3MD) distributions. The models are evaluated with radar data collected from a ground-based bistatic radar system and two experimental airborne radars. These data sets cover a wide range of frequencies (L-, S-, and X-band), and different collection geometries and sea conditions. To guide the selection of the most appropriate model, two goodness of fit metrics are used, the Bhattacharyya distance which measures the overall distribution error and the threshold error which quantifies mismatch in the distribution tail. Together, they allow a quantitative evaluation of each distribution to accurately model radar sea clutter for the purpose of radar ship detection. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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28 pages, 14784 KiB

Open AccessArticle

Operational Monitoring of Illegal Fishing in Ghana through Exploitation of Satellite Earth Observation and AIS Data

by Andrey A. Kurekin, Benjamin R. Loveday, Oliver Clements, Graham D. Quartly, Peter I. Miller, George Wiafe and Kwame Adu Agyekum

Remote Sens. 2019, 11(3), 293; https://doi.org/10.3390/rs11030293 - 01 Feb 2019

Cited by 65 | Viewed by 15234

Abstract

Over the last decade, West African coastal countries, including Ghana, have experienced extensive economic damage due to illegal, unreported and unregulated (IUU) fishing activity, estimated at about USD 100 million in losses each year. Illegal, unreported and unregulated fishing poses an enormous threat to the conservation and management of the dwindling fish stocks, causing multiple adverse consequences for fisheries, coastal and marine ecosystems and for the people who depend on these resources. The Integrated System for Surveillance of Illegal, Unlicensed and Unreported Fishing (INSURE) is an efficient and inexpensive system that has been developed for the monitoring of IUU fishing in Ghanaian waters. It makes use of fast-delivery Earth observation data from the synthetic aperture radar instrument on Sentinel-1 and the Multi Spectral Imager on Sentinel-2, detecting objects that differ markedly from their immediate background using a constant false alarm rate test. Detections are matched to, and verified by, Automatic Identification System (AIS) data, which provide the location and dimensions of ships that are legally operating in the region. Matched and unmatched data are then displayed on a web portal for use by coastal management authorities in Ghana. The system has a detection success rate of 91% for AIS-registered vessels, and a fast throughput, processing and delivering information within 2 h of acquiring the satellite overpass. However, over the 17-month analysis period, 75% of SAR detections have no equivalent in the AIS record, suggesting significant unregulated marine activity, including vessels potentially involved in IUU. The INSURE system demonstrated its efficiency in Ghana’s exclusive economic zone and it can be extended to the neighbouring states in the Gulf of Guinea, or other geographical regions that need to improve fisheries surveillance. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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22 pages, 11229 KiB

Open AccessArticle

Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

by Bangyan Zhu, Xiao Wang, Zhengwei Chu, Yi Yang and Juan Shi

Remote Sens. 2019, 11(3), 243; https://doi.org/10.3390/rs11030243 - 24 Jan 2019

Cited by 32 | Viewed by 5076

Abstract

In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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26 pages, 6881 KiB

Open AccessArticle

Detection, Localization and Classification of Multiple Mechanized Ocean Vessels over Continental-Shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing

by Chenyang Zhu, Heriberto Garcia, Anna Kaplan, Matthew Schinault, Nils Olav Handegard, Olav Rune Godø, Wei Huang and Purnima Ratilal

Remote Sens. 2018, 10(11), 1699; https://doi.org/10.3390/rs10111699 - 29 Oct 2018

Cited by 22 | Viewed by 4172

Abstract

Multiple mechanized ocean vessels, including both surface ships and submerged vehicles, can be simultaneously monitored over instantaneous continental-shelf scale regions >10,000 km

^{2}

via passive ocean acoustic waveguide remote sensing. A large-aperture densely-sampled coherent hydrophone array system is employed in the Norwegian Sea [...] Read more.

Multiple mechanized ocean vessels, including both surface ships and submerged vehicles, can be simultaneously monitored over instantaneous continental-shelf scale regions >10,000 km

^{2}

via passive ocean acoustic waveguide remote sensing. A large-aperture densely-sampled coherent hydrophone array system is employed in the Norwegian Sea in Spring 2014 to provide directional sensing in 360 degree horizontal azimuth and to significantly enhance the signal-to-noise ratio (SNR) of ship-radiated underwater sound, which improves ship detection ranges by roughly two orders of magnitude over that of a single hydrophone. Here, 30 mechanized ocean vessels spanning ranges from nearby to over 150 km from the coherent hydrophone array, are detected, localized and classified. The vessels are comprised of 20 identified commercial ships and 10 unidentified vehicles present in 8 h/day of Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) observation for two days. The underwater sounds from each of these ocean vessels received by the coherent hydrophone array are dominated by narrowband signals that are either constant frequency tonals or have frequencies that waver or oscillate slightly in time. The estimated bearing-time trajectory of a sequence of detections obtained from coherent beamforming are employed to determine the horizontal location of each vessel using the Moving Array Triangulation (MAT) technique. For commercial ships present in the region, the estimated horizontal positions obtained from passive acoustic sensing are verified by Global Positioning System (GPS) measurements of the ship locations found in a historical Automatic Identification System (AIS) database. We provide time-frequency characterizations of the underwater sounds radiated from the commercial ships and the unidentified vessels. The time-frequency features along with the bearing-time trajectory of the detected signals are applied to simultaneously track and distinguish these vessels. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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22 pages, 4469 KiB

Open AccessArticle

A Ship Detector Applying Principal Component Analysis to the Polarimetric Notch Filter

by Tao Zhang, Armando Marino, Huilin Xiong and Wenxian Yu

Remote Sens. 2018, 10(6), 948; https://doi.org/10.3390/rs10060948 - 14 Jun 2018

Cited by 10 | Viewed by 3848

Abstract

Ship detection using polarimetric synthetic aperture radar (PolSAR) data has attracted a lot of attention in recent years. Polarimetry can provide information regarding the scattering mechanisms of targets, which helps discriminate between ships and sea clutter. This enhancement is particularly valuable when we aim at detecting smaller vessels in rough sea states. This work exploits a ship detector called the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF), and it is aimed at improving its performance especially when less polarimetric images are available (e.g., dual-polarimetric data). The idea is to design a new polarimetric feature vector containing more features that are renowned to allow separation between ships and sea clutter. Then, a Principal Component Analysis (PCA) is further used to reduce the dimensionality of the new feature space. Experiments on four real Sentinel-1 datasets are carried out to demonstrate the validity of the proposed method and compare it against other ship detectors. Analyses of the experimental results show that the proposed algorithm can not only reduce the false alarms significantly, but also enhance the target-to-clutter ratio (TCR) so that it can more effectively detect weaker ships. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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14 pages, 4574 KiB

Open AccessArticle

Quantification of Typhoon-Induced Phytoplankton Blooms Using Satellite Multi-Sensor Data

by Jiayi Pan, Lei Huang, Adam T. Devlin and Hui Lin

Remote Sens. 2018, 10(2), 318; https://doi.org/10.3390/rs10020318 - 20 Feb 2018

Cited by 37 | Viewed by 4050

Abstract

Using satellite-based multi-sensor observations, this study investigates Chl-a blooms induced by typhoons in the Northwest Pacific (NWP) and the South China Sea (SCS), and quantifies the blooms via wind-induced mixing and Ekman pumping parameters, as well as pre-typhoon mixed-layer depth (MLD). In the NWP, the Chl-a bloom is more correlated with the Ekman pumping than with the other two parameters, with an R² value of 0.56. In the SCS, the wind-induced mixing and Ekman pumping have comparable correlations with the Chl-a increase, showing R² values of 0.4~0.6. However, the MLD exhibits a negative correlation with the Chl-a increase. A multi-parameter quantification model of the Chl-a bloom strength achieves better results than the single-parameter regressions, yielding a more significant R² value of 0.80, and a lower regression rms of 0.18 mg·m⁻³ in the SCS, and the R² value in the NWP is also improved compared with the single-parameter regressions. The multi-parameter quantification model of Chl-a blooms is more accurate in the SCS than in the NWP, due to the fact that nutrient profiles in the NWP are uniform from surface to a deep depth (300 m). Thus, the Chl-a blooms are more correlated with the upper ocean dynamical processes in the SCS where a shallower nutricline is found. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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16 pages, 30265 KiB

Open AccessArticle

A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps

by Xiao Wang, Jianhu Zhao, Bangyan Zhu, Tingchen Jiang and Tiantian Qin

Remote Sens. 2018, 10(2), 295; https://doi.org/10.3390/rs10020295 - 14 Feb 2018

Cited by 26 | Viewed by 6683

Abstract

To accurately achieve side scan sonar (SSS) image target detection, a novel target detection algorithm based on a neutrosophic set (NS) and diffusion maps (DMs) is proposed in this paper. Firstly, the neutrosophic subset images were obtained by transforming the input SSS image into the NS domain. Secondly, the shadowed areas of the SSS image were detected using the single gray value threshold method before the diffusion map was calculated. Lastly, based on the diffusion map, the target areas were detected using the improved target scoring equation defined by the diffusion distance and texture feature. The experiments using SSS images of single clear and unclear targets, with or without shadowed areas, showed that the algorithm accurately detects targets. Experiments using SSS images of multiple targets, with or without shadowed areas, showed that no false or missing detections occurred. The target areas were also accurately detected in SSS images with complex features such as sand wave terrain. The accuracy and effectiveness of the proposed algorithm were assessed. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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23 pages, 3425 KiB

Open AccessArticle

Ship Classification Based on MSHOG Feature and Task-Driven Dictionary Learning with Structured Incoherent Constraints in SAR Images

by Huiping Lin, Shengli Song and Jian Yang

Remote Sens. 2018, 10(2), 190; https://doi.org/10.3390/rs10020190 - 27 Jan 2018

Cited by 38 | Viewed by 5194

Abstract

In this paper, we present a novel method for ship classification in synthetic aperture radar (SAR) images. The proposed method consists of feature extraction and classifier training. Inspired by SAR-HOG feature in automatic target recognition, we first design a novel feature named MSHOG by improving SAR-HOG, adapting it to ship classification, and employing manifold learning to achieve dimensionality reduction. Then, we train the classifier and dictionary jointly in task-driven dictionary learning (TDDL) framework. To further improve the performance of TDDL, we enforce structured incoherent constraints on it and develop an efficient algorithm for solving corresponding optimization problem. Extensive experiments performed on two datasets with TerraSAR-X images demonstrate that the proposed method, MSHOG feature and TDDL with structured incoherent constraints, outperforms other existing methods and achieves state-of-art performance. Full article

(This article belongs to the Special Issue Remote Sensing of Target Detection in Marine Environment)

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