First paragraph: 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. Measurements collected by active and passive remote sensing instruments can be used for both marine and maritime applications. They allow monitoring of vast areas of the Earth that are difficult to access and sample using traditional methods. Within this context, the observation of targets at sea, e.g.; man-made targets (ships or oil/gas rigs/platforms and wind turbines) and natural targets (icebergs, surfactants, etc.) is nowadays a very hot-topic in the field of global monitoring of environment and security

Marino, Armando

Nunziata, Ferdinando

Velotto, Domenico

English

Stirling Online Research Repository

remote sensing  EditorialEditorial for the Special Issue “Remote Sensing ofTarget Detection in Marine Environment”Ferdinando Nunziata 1,* , Armando Marino 2 and Domenico Velotto 31 Dipartimento di Ingnegneria, Università degli Studi di Napoli Parthenope, Centro Direzionale,isola C4-80143 Napoli, Italy2 Natural Sciences, The University of Stirling, Stirling FK9 4LA, UK3 Maritime Safety and Security Lab, Remote Sensing Technology Institute, German Aerospace Center (DLR),28199 Bremen, Germany* Correspondence: ferdinando.nunziata@uniparthenope.it; Tel.: +39-081-5476779Received: 5 July 2019; Accepted: 15 July 2019; Published: 17 July 2019Remote sensing is a powerful tool used to obtain an unprecedented amount of information aboutthe ocean from a distance, usually from satellites or aircrafts. Measurements collected by activeand passive remote sensing instruments can be used for both marine and maritime applications.They allow monitoring of vast areas of the Earth that are difficult to access and sample using traditionalmethods. Within this context, the observation of targets at sea, e.g.; man-made targets (ships or oil/gasrigs/platforms and wind turbines) and natural targets (icebergs, surfactants, etc.) is nowadays a veryhot-topic in the field of global monitoring of environment and security.Among the remote sensing tools, Synthetic Aperture Radar (SAR) is one of the most used sinceit allows all-day and almost all-weather observations with a moderate-to-fine spatial resolution.SAR imagery gives the possibility to overcome the limits of maritime patrol allowing non-cooperativeall-day target surveillance, over wide regions and under almost all-weather conditions. An increasingnumber of SAR satellites have become available since the early 1990s. This unprecedented developmentin 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 literaturethat concerns SAR methods for detecting targets at sea, there is still room to improve both modelsand methods.Within this context, methods and models are proposed in this Special Issue (SI) to exploitsingle- and multi-polarization SAR measurements for observation of targets at sea, with special interestfor vessels.In ref. [1] the complex coherence between the two channels of the dual-polarized Sentinel-1 SARimagery is exploited to detect ships in a timely manner using the entire image. The proposed rationale isverified against real SAR imagery that includes a large number of vessels of different sizes. In addition,Automatic Identification System (AIS) data are used for an extensive and large-scale cross-comparisonwith the SAR-based detections. Experimental results clearly point out the remarkable detection rate ofthe SAR-based method and its complementarity with respect to AIS information.In ref. [2] fishing vessels detection is addressed with reference to the problem of global overfishing.A method is here developed to deal with the discrimination between fishing and non-fishing vesselsand a showcase is presented. The method, which is based on the Random Forest (RF) classifier, takesas input the vessel’s length, longitude, and latitude, its distance to the nearest shore, and the time ofthe measurement (am or pm). The classifier is trained and tested on data from the AIS and Sentinel-1SAR imagery that refers to the North Sea.In ref. [3] a system to monitor illegal fishing activities is proposed. The approach, which isdeveloped in the framework of a cooperation between the Plymouth Marine Laboratory (PML) andRemote Sens. 2019, 11, 1689; doi:10.3390/rs11141689 www.mdpi.com/journal/remotesensingRemote Sens. 2019, 11, 1689 2 of 3the Economic Community of West African States Coastal and Marine Resources Management Centre(ECOWAS Marine Centre), uses freely available Sentinel-1 SAR and Sentinel-2 Multispectral Instrument(MSI) optical data. The designed algorithms deliver the vessel information to customers through adedicated web portal interface. The system merges vessel detections in satellite images with AIS datato identify non-cooperative vessels that can be involved in illegal fishing activities.In ref. [4] ship detection at sea is addressed using dual-polarimetric SAR measurements. A detectionscheme that is based on the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF) is used todetect ships in dual-polarimetric Sentinel-1 SAR scenes. The proposed rationale consists of buildinga new feature vector, which is richer than the conventional one in terms of polarimetric descriptors,and using it to enhance the target to clutter ratio (TCR).In ref. [5] a new method is proposed to address ship classification in SAR imagery that is based onthe combination of an improved SAR–HOG method and a manifold learning methodology to reducethe dimensionality of the problem. Experiments undertaken on actual TerraSAR-X scenes confirm thesoundness of the proposed rationale.Although the contributions to this SI are mainly focused on methods to detect/classify marinevessels using SAR measurements, additional microwave observation techniques are also discussed.In ref. [6] a fast and effective approach is developed to detect ship in range-compressed radar dataacquired by an airborne radar sensor during linear, circular, and arbitrary flight tracks. The approachis developed to work in a real-time-oriented way by exploiting a constant false alarm rate (CFAR)detection threshold that is computed in the range-Doppler domain together with an automatic oceantraining data extraction procedure. The soundness of the proposed approach is validated againstlinearly and circularly acquired measurements collected by the DLR’s (Deutsches Zentrum für Luft- undRaumfahrt) airborne F-SAR system.In ref. [7] target detection at over the horizon (OTH) distances is addressed using a network ofhigh frequency surface-wave-radars (HFSWR). Since a vessel is typically observed by multiple HFSWR,there may be very often situations in which multiple tracks for a vessel are present. Hence, in thisstudy, a method is proposed to merge radar tracks obtained and elaborated at the single radar levelinto a unique data stream. Experiments, undertaken on actual HFSWR measurements and verifiedusing AIS ancillary information, demonstrate that the proposed approach provides accurate tracking,preventing the creation of duplicate targets and it delivers a unique and very computationally efficientradar picture at OTH distances.In ref. [8] the accuracy of some of the most well-known models for statistical distribution of seaclutter amplitude are verified against a broad range of environmental conditions, radar geometries andconfigurations. This is done using actual measurements collected by mono- and bi-static ground-basedand airborne multi-polarization radars operating at different frequencies. The model’s accuracy isassessed using state-of-the-art metrics.In addition to microwave remote sensing instruments, acoustic probes are also exploitedby proposing new methods to process Side Scan Sonar (SSS) imagery and acoustic waveguidemeasurements for vessels observation purposes.In ref. [9] shipwreck recognition in side-scan sonar (SSS) maps is addressed and a processingchain that consists of feature extraction, feature selection, and shipwreck recognition is proposed andverified using real SSS sample measurements.In ref. [10] ships and submerged marine vehicles are observed via passive ocean acousticwaveguide remote sensing. They provide time-frequency characteristics of the dominant narrowbandsignals received from a number of distinct ocean vessels detected at varying ranges from the coherenthydrophone array. The detections are verified using GPS information provided by an historicalAIS database.In ref. [11] a novel algorithm based on a neutrosophic set (NS) and diffusion maps (DMs) isproposed to deal with target detection in SSS imagery. The accuracy and effectiveness of the proposedRemote Sens. 2019, 11, 1689 3 of 3algorithm is verified using actual SSS imagery showing that the proposed rationale succeeds indetecting targets even against complicated backgrounds.In addition to sea vessels observation, two contributions addressed topics that are closely relatedto vessels monitoring: the observation of icebergs and blooms.In ref. [12] remotely sensed measurements collected by both SAR and radar altimeter are used toobserve the giant tabular iceberg A68 that broke away from the Larsen C Ice Shelf, Antarctic Peninsula,in July 2017. The initial evolution of iceberg A68A—the largest originating from A68—in terms ofchanges in its area, drift speed, rotation, and freeboard is analyzed using Sentinel-1 SAR imagery andCryoSat-2 SAR/Interferometric Radar Altimeter observations.In ref. [13] the effect of Ekman pumping, wind-induced mixing parameters, and the pre-typhoonmixed layer depth on typhoon-induced phytoplankton blooms is analyzed in the Northwest Pacific(NWP) and the South China Sea (SCS) using satellite remotely sensed data collected by different sensors.Conflicts of Interest: The authors declare no conflict of interest.References1. Pelich, R.; Chini, M.; Hostache, R.; Matgen, P.; Lopez-Martinez, C.; Nuevo, M.; Ries, P.; Eiden, G. Large-ScaleAutomatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data. Remote Sens. 2019, 11,1078. [CrossRef]2. Snapir, B.; Waine, T.W.; Biermann, L. Maritime Vessel Classification to Monitor Fisheries with SAR:Demonstration in the North Sea. Remote Sens. 2019, 11, 353. [CrossRef]3. Kurekin, A.A.; Loveday, B.R.; Clements, O.; Quartly, G.D.; Miller, P.I.; Wiafe, G.; Adu Agyekum, K.Operational Monitoring of Illegal Fishing in Ghana through Exploitation of Satellite Earth Observation andAIS Data. Remote Sens. 2019, 11, 293. [CrossRef]4. Zhang, T.; Marino, A.; Xiong, H.; Yu, W. A Ship Detector Applying Principal Component Analysis to thePolarimetric Notch Filter. Remote Sens. 2018, 10, 948. [CrossRef]5. Lin, H.; Song, S.; Yang, J. Ship Classification Based on MSHOG Feature and Task-Driven Dictionary Learningwith Structured Incoherent Constraints in SAR Images. Remote Sens. 2018, 10, 190. [CrossRef]6. Joshi, S.K.; Baumgartner, S.V.; da Silva, A.B.C.; Krieger, G. Range-Doppler Based CFAR Ship Detection withAutomatic Training Data Selection. Remote Sens. 2019, 11, 1270. [CrossRef]7. Nikolic, D.; Stojkovic, N.; Popovic, Z.; Tosic, N.; Lekic, N.; Stankovic, Z.; Doncov, N. Maritime Over theHorizon Sensor Integration: HFSWR Data Fusion Algorithm. Remote Sens. 2019, 11, 852. [CrossRef]8. Angelliaume, S.; Rosenberg, L.; Ritchie, M. Modeling the Amplitude Distribution of Radar Sea Clutter.Remote Sens. 2019, 11, 319. [CrossRef]9. Zhu, B.; Wang, X.; Chu, Z.; Yang, Y.; Shi, J. Active Learning for Recognition of Shipwreck Target in Side-ScanSonar Image. Remote Sens. 2019, 11, 243. [CrossRef]10. Zhu, C.; Garcia, H.; Kaplan, A.; Schinault, M.; Handegard, N.O.; Godø, O.R.; Huang, W.; Ratilal, P. Detection,Localization and Classification of Multiple Mechanized Ocean Vessels over Continental-Shelf Scale Regionswith Passive Ocean Acoustic Waveguide Remote Sensing. Remote Sens. 2018, 10, 1699. [CrossRef]11. Wang, X.; Zhao, J.; Zhu, B.; Jiang, T.; Qin, T. A Side Scan Sonar Image Target Detection Algorithm Based on aNeutrosophic Set and Diffusion Maps. Remote Sens. 2018, 10, 295. [CrossRef]12. Han, H.; Lee, S.; Kim, J.; Kim, S.H.; Kim, H. Changes in a Giant Iceberg Created from the Collapse of theLarsen C Ice Shelf, Antarctic Peninsula, Derived from Sentinel-1 and CryoSat-2 Data. Remote Sens. 2019, 11,404. [CrossRef]13. Pan, J.; Huang, L.; Devlin, A.T.; Lin, H. Quantification of Typhoon-Induced Phytoplankton Blooms UsingSatellite Multi-Sensor Data. Remote Sens. 2018, 10, 318. [CrossRef]© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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

Stirling Online Research Repository (RIOXX)

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 [...

Ferdinando Nunziata

Multidisciplinary Digital Publishing Institute

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

Institute of Transport Research:Publications

http://dspace.stir.ac.uk/bitstream/1893/30084/1/Nunziata%20et%20al-RS-2019.pdf

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

Abstract

Similar works

Full text

Available Versions

Stirling Online Research Repository

Stirling Online Research Repository (RIOXX)

Multidisciplinary Digital Publishing Institute

Institute of Transport Research:Publications