multi-satellite data integration for operational ship

Defence Research and Development Canada Contract Report DRDC-RDDC-2019-C064 April 2019

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Multi-Satellite Data Integration for Operational Ship Detection, Identification and Tracking (DIOS) Progress Report 1

Sherry Warren C-CORE Igor Zakharov C-CORE Prepared by: C-CORE 4043 Carling Ave., Suite 202 Ottawa, Ontario Canada K2K 2A4 R-18-044-1443 PSPC Contract Number: W7714-186608/001/sv Technical Authority: Daniel Lavigne, Defence Scientist Contractor's date of publication: January 2019

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© Her Majesty the Queen in Right of Canada (Department of National Defence), 2019 © Sa Majesté la Reine en droit du Canada (Ministère de la Défense nationale), 2019

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IMPORTANT INFORMATIVE STATEMENTS

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Disclaimer: This document is not published by the Editorial Office of Defence Research and Development Canada, an agency of the Department of National Defence of Canada but is to be catalogued in the Canadian Defence Information System (CANDIS), the national repository for Defence S&T documents. Her Majesty the Queen in Right of Canada (Department of National Defence) makes no representations or warranties, expressed or implied, of any kind whatsoever, and assumes no liability for the accuracy, reliability, completeness, currency or usefulness of any information, product, process or material included in this document. Nothing in this document should be interpreted as an endorsement for the specific use of any tool, technique or process examined in it. Any reliance on, or use of, any information, product, process or material included in this document is at the sole risk of the person so using it or relying on it. Canada does not assume any liability in respect of any damages or losses arising out of or in connection with the use of, or reliance on, any information, product, process or material included in this document.

Multi-Satellite Data Integration for Operational Ship Detection,

Identification and Tracking (DIOS) Progress Report 1

C-CORE Document Number R-18-044-1443

Prepared for: Defence Research and Development Canada (DRDC)

Revision 1.0 January, 2019

This page is intentionally left blank

Multi-Satellite Data Integration for Operational Ship Detection, Identification and Tracking (DIOS), Progress Report 1 PREPARED FOR Defence Research and Development Canada (DRDC) DOC ID R-18-044-1443 REVISION 1.0 DATE January, 2019

The correct citation for this document is: C-CORE. 2019. “Multi-Satellite Data Integration for Operational Ship Detection, Identification and Tracking (DIOS).” Proposal R-18-044-1443, Revision 1.0. Project Team Sherry Warren (Project Manager) Des Power Thomas Puestow Igor Zakharov Mark Kapfer Mark Howell Michael Lynch Pamela Burke Robert Hewitt


Revision History

Version Name Date Of Changes Comments

1.0 Sherry Warren Igor Zakharov 01/25/19 Submitted to Client

Distribution List

Company Name Number Of Copies

Defence Research and Development Canada (DRDC)

Daniel Lavigne Paris Vachon Mike Sale

1 electronic


DIOS Progress Report 1 i

Table of Contents

1 INTRODUCTION .................................................................................................................................... 1

1.1 Background and Objectives ........................................................................................................ 1

2 PROJECT MANAGEMENT (WP 1000) .................................................................................................... 3

2.1 Methodology ............................................................................................................................... 3 2.2 Work Package Descriptions (WBS) .............................................................................................. 4 2.3 Schedule and Budget .................................................................................................................. 5 2.4 Deliverables ................................................................................................................................. 5

3 LITERATURE/TECHNOLOGY REVIEW (WP 2000) .................................................................................. 8

3.1 Target Detection Approaches Using SAR, EO/IR and Radar Altimeter Sensors .......................... 8 3.1.1 SAR Satellites ................................................................................................................... 8

3.1.1.1 Relevant C-Band CP Technology ......................................................................... 8 3.1.1.2 X-Band SAR Sensors ............................................................................................ 9 3.1.1.3 SAR-Based Ship Detection ................................................................................ 10

3.2 EO/IR Sensors ............................................................................................................................ 11 3.2.1 Ship Detection Using High-Resolution EO/IR Sensors ................................................... 11

3.2.1.1 Ship Detection Using Medium-Resolution EO/IR Sensors ................................ 14 3.2.1.2 Ship Detection Using Low Resolution EO/IR Sensors ....................................... 15

3.2.2 Radar Altimeters ............................................................................................................ 16 3.2.2.1 Current Radar Altimeters ................................................................................. 16 3.1.4.1 Altimetry-Based Ship Detection ....................................................................... 17

3.3 Sensor-Specific Feature Extraction ........................................................................................... 17 3.3.1 Classification of Vessel and Non-Vessel Targets............................................................ 17

3.3.1.1 SAR Feature Extraction and Target Decomposition ......................................... 18 3.3.1.2 Feature Extraction in EO/IR .............................................................................. 19 3.3.1.3 Feature Extraction in Altimetry Data ................................................................ 20

3.4 Image-Based Ship and Iceberg Tracking ................................................................................... 21 3.4.1 Ship Tracking .................................................................................................................. 21 3.4.2 Iceberg Tracking ............................................................................................................. 21

3.5 Recommendations for DIOS ...................................................................................................... 22 3.5.1.1 Detection of Ships in SAR Imagery ................................................................... 23 3.5.1.2 Detection of Ships in Altimetry Data ................................................................ 23 3.5.1.3 Detection of Ships in EO/IR Data ...................................................................... 23 3.5.1.4 Discrimination of Ship from Non-Ship Targets ................................................. 23 3.5.1.5 Summary of Recommendations ....................................................................... 24

3.6 DIOS Project Results Relevant to DND and CAF ........................................................................ 24

4 FUTURE WORK ................................................................................................................................... 25

5 REFERENCES ....................................................................................................................................... 26


DIOS Progress Report 1 ii

APPENDIX A: DND AND CAF REPORT .......................................................................................................... 31


DIOS Progress Report 1 iii

List of Tables

Table 1. Work Packages ................................................................................................................................ 5 Table 2. List of Project Management Deliverables ....................................................................................... 6 Table 3. List of Work Package Deliverables .................................................................................................. 6 Table 4. List of Recommendations .............................................................................................................. 24

List of Figures

Figure 1. Flowchart of multi-satellite data integration for ship detection, classification, identification and tracking. ................................................................................................................................................ 4

Figure 2. Project Schedule ............................................................................................................................ 5 Figure 3. A common scheme of vessel detection workflow (Kanjir et al. 2018). ....................................... 12 Figure 4. Ship detection with strong ocean waves: Original SPOT 5 image (on the left) and ship detected

with method described by (Zhu et al. 2010) (on the right). ............................................................... 13 Figure 5. Ship detection based on visual saliency model (Dong, Liu, and Xu 2018). Input VHR image (left),

probability distributions (middle) for patches A-C, saliency model (right). ....................................... 13 Figure 6. Sentinel-2 based detection over the capital Nuuk of Greenland (Heiselberg and Heiselberg 2017).

............................................................................................................................................................ 15 Figure 7. DNB of S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) with icebreaker Taimyr on 18

Nov. 2013 (Straka et al. 2015). ........................................................................................................... 16


DIOS Progress Report 1 1

1 Introduction This document comprises Progress Review Report 1 as a deliverable under Defence Industrial Research Program (DIRP) Contract No. W7714-186608/001/sv. It provides an update on the technical and management progress for Multi-Satellite Data Integration for Operational Ship detection, identification and tracking (DIOS) since the kick-off meeting (KOM) held on September 6, 2018.

1.1 Background and Objectives DIOS addresses the Maritime Surveillance Strategic Objective 5 (SO5) by using electro-optical and infrared (EO/IR) sensors to compliment the RADARSAT Constellation Mission (RCM) mission for ship detection, identification, classification and tracking. This project will investigate new ways of monitoring shipping traffic and icebergs using multiple sensors, including RADARSAT-2 (RS-2) and TerraSAR-X (TSX). It also builds on previous DIRPs and the current DIRP titled “Tactical Detection of Icebergs and Ships in Sea Ice for Polar Epsilon 2 (TACTICS-PE2)”. DIOS will build on what was learned from the previous TACTICS/ShipTac project to increase ship detection and discrimination using a multi-satellite data integration approach using C-Band and X-Band, EO/IR, and radar altimetry.

C-CORE will investigate and develop a multi-satellite data classifier that determines if an unknown target is a ship or an iceberg. C-CORE will deliver a Technical Note (TN) that describes the classifier algorithm that can be exploited by Polar Epsilon 2 (PE2) software developers1. Further to the RCM, DIOS will also investigate TSX X-Band capabilities, EO/IR and radar altimetry and analyze how these can improve PE2 and DRDC operations. This combination of remote sensing technologies can provide improved knowledge and additional details not provided by using Synthetic Aperture Radar (SAR) imagery alone. DIOS will demonstrate how these types of technologies can benefit detection, discrimination, identification and tracking of ships and icebergs in open water.

DIOS will deliver maritime surveillance tools to PE2 to address a specific capability gap. That gap is in PE2’s ability to monitor ships using RCM with complimentary high resolution X-Band SAR and EO/IR data. The monitoring areas will include ports and coastal areas, where RCM data alone have limitations. The use of multi-satellite imaging data supplemented with the radar altimeters can increase temporal frequency of acquisitions for certain regions. C-CORE’s work scope will deliver algorithms that optimally detect targets then classify those targets into vessels and false alarms. In this context, false alarms may be caused by icebergs or other ocean features. C-CORE proposes that the algorithms developed within DIOS be delivered as a TN so that PE2 software developers can implement the algorithms within OceanSuite without further input from C-CORE’s software developers. In parallel, C-CORE will investigate techniques to implement the algorithms within its own Iceberg Detection Software (IDS) to facilitate an improved commercial SAR surveillance service to its oil and gas (O&G) clients. C-CORE will demonstrate the enhanced detection capability in an operational context with one of its O&G clients. The demonstration

1 In delivering the algorithm as a TN, the project is not disruptive to the existing PE2 software development process. PE2 have already engaged a third party contractor to develop a new version of OceanSuite, which is the tool that PE2 will use to analyze RCM data for ship targets. Thus, it is logical to deliver the algorithm in a form of a TN to that contractor for implementation.



will allow the evaluation of several performance measures on the value of the new innovation, in terms of both detection and classification performance.

The operational ship detection, classification, identification and tracking using multi-satellite data can potentially provide significantly more information about ships than any single type of satellites, but it is a complex problem that requires a solution of several tasks. Capabilities of individual sensors, including different bands and modes, for automated extraction and analysis of features specific for ship and non-ship targets have to be investigated. Automated feature extraction algorithms and methods for target classification and ship identification (i.e. determination of ship size and type) using data from two or more satellites, including ship simultaneous appearance in multi-sensor data, have to be investigated and developed. Automated algorithms for multi-sensor and multi-temporal data analysis and integration into IDS or other prototype software is required for ship/iceberg tracking and false alarm reduction.

The main objectives of the DIOS project include the following:

To develop a capability for EO/IR sensors to compliment the RCM mission for ship detection, identification, classification and tracking; To use TSX, EO/IR and radar altimetry to help contribute new knowledge to DRDC and their stakeholders; To investigate new ways of monitoring shipping traffic and icebergs in open water using multiple sensors; and To investigate and develop a multi-satellite data classifier to characterize satellite detections as ships, icebergs or other non-ship targets.



2 Project Management (WP 1000) The following subsections provide details on the methodology, the major work packages (WPs) that will be performed during this project and an update on project management items including schedule, budget and deliverables.

2.1 Methodology C-CORE’s integrated methodology for DIOS is shown in Figure 1 and consists of three major steps of data manipulation:

Satellite data collection; Pre-processing and target detection; and Integration, including sensor data fusion, target identification and tracking.

Satellite data collection includes acquisitions previously planned by a satellite operator and tasking for the purpose of DIOS project. The VHR satellite data, such as Pleiades and SPOT will be tasked for new acquisitions over the selected study areas. Pre-planned satellite acquisitions will include:

SAR: Sentinel-1A/1B (S-1A and S-1B); Medium Resolution (MR) EO/IR: Sentinel-2A/2B (S-2A and S-2B), Landsat-8 (L-8); Low resolution (LR) EO/IR: MODIS on Aqua and Terra, Suomi NPP, and Sentinel-3 (S-3); and Altimeters.

Pre-processing techniques will be performed, which includes calibration and cloud masking on the EO/IR data. Target detection algorithms will include the CFAR detection with IDS and detections algorithm for EO/IR. Target detection for altimetry data is based on a combination of the Fast-Fourier Transform and thresholding. Recently, C-CORE developed a capability for automated land-based target and change detection using Very High Resolution (VHR) and Medium Resolution (MR) EO/IR satellites that requires minimum analyst input. This technology underwent pre-operational evaluation for corridor monitoring (Zakharov et al. 2016) and is directly relevant to the DIOS initiative.

The final integration stage will include feature extraction and the combination of the target classification algorithm outputs with the tracking information provided by Airbus. The final DIOS algorithm will distinguish ships from non-ship targets.



Figure 1. Flowchart of multi-satellite data integration for ship detection, classification, identification and tracking.

2.2 Work Package Descriptions (WBS) The project objectives will be addressed by executing the tasks shown in Table 1. The following sections describe work conducted under from WP 1000 and WP 2000. WP 3000 to WP 8000 have not yet commenced. C-CORE is responsible for the overall project management and technical work packages, including WP 2000 to WP 4000, WP 7000 and WP 8000. Airbus is responsible for WP 5000 and WP 6000.

Integration

Satellite data collection

SAR C-Band: S-1A, S-1B, R-2, RCM X-Band:TSX1, TDX1

EO/IR: VHR: Pleiades, SPOT-6, SPOT-7 MR: S-2A, S-2B, L-8 LR: MODIS (Aqua& Terra), Suomi NPP, S-3

Altimeters: Jason-2, Jason-3, SIRAL/Altika

Pre-processing & Target Detection

CFAR detection with IDS Detection

algorithm in OE/IR

AID Calibration and cloud masking

Feature extraction and Target Classification

Identification and Tracking

IDS classifier EO/IR classification

Altimetry classification algorithm

Fusion and identification

Ships and non-ship targets

Tracking

AIS



Table 1. Work Packages

Work Package Task Description 1000 Project Management 2000 Literature/Technology Review 3000 Data Collection and Validation 4000 Algorithm Development for Ship Classification 5000 Multi-Sensor Tracking (Airbus) 6000 Ship Identification (Airbus) 7000 IDS Implementation 8000 Results Consolidation

2.3 Schedule and Budget The KOM was held on September 6, 2018 at the Airbus office in Ottawa. Preliminary work on DIOS began immediately following the KOM. There are no changes to the baseline schedule, as shown in Figure 2. DIOS is a 23 month project and is scheduled to end on July 31, 2020. Progress meetings will be conducted every four months to allow for frequent review and feedback. The progress review meetings will serve as Go/No-Go points to cancel or redirect project activities, if necessary.

The project is currently on budget with approximately $60,000 spent to date and a target of $145,000 to be expended by March 31st, 2019.

Figure 2. Project Schedule KO = Kick off, PR = Progress Review Meeting, FM = Final Meeting

2.4 Deliverables The project has the following progress meetings and reports scheduled based on the statement of work. A complete list of project management deliverables is provided in Table 2. Progress meeting dates may change and are subject to schedule availabilities of the Project Authorities and Sponsors. The WP deliverables are presented in Table 3. All associated reports and deliverables will be submitted to DRDC via the project’s SharePoint.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23YearTask Description S O N D J F M A M J J A S O N D J F M A M J JWP1000 Project Management KO PR PR PR PR PR FMWP2000 Literature/Technology ReviewWP3000 Data Collection and ValidationWP4000 Algorithm Development for Ship ClassificationWP5000 Multi-Sensor Tracking (Airbus)WP6000 Ship Identification (Airbus)WP7000 IDS ImplementationWP8000 Results Consolidation

2018 2019 2020



Table 2. List of Project Management Deliverables

Item Deliverable Planned Date Delivery Date/Status MPR Monthly Progress Reports Ongoing Ongoing KOM Kick-Off Meeting September 6, 2018 September 6, 2018 PR1 Progress Review Meeting 1 January 31, 2019 January 31, 2019 D1 Progress Review Report 1 January 24, 2019 Draft submitted

D2 Progress Review Meeting 1 Agenda, Presentation and Minutes February 8, 2019 Pending

PR2 Progress Review Meeting 2 May 2019 D3 Progress Review Report 2 May 2019

D4 Progress Meeting 2 Agenda, Presentation and Minutes May 2019

PR3 Progress Review Meeting 3 September 2019 D5 Progress Review Report 3 September 2019

D6 Progress Review Meeting 3 Agenda, Presentation and Minutes September 2019

PR4 Progress Review Meeting 4 January 2020 D7 Progress Review Report 4 January 2020

D8 Progress Review Meeting 4 Agenda, Presentation and Minutes

January 2020

PR5 Progress Review Meeting 5 May 2020 D9 Progress Review Report 5 May 2020

D10 Progress Review Meeting 5 Agenda, Presentation and Minutes

May 2020

D11 Final Report July 2020 D12 Final Review Agenda, Presentation and Minutes July 2020 D13 OceanSuite Technical Note July 2020 FRM Final Review Meeting July 2020 D14 Project Close Out July 2020

Table 3. List of Work Package Deliverables

Work Package Item Deliverable 2000 WP1 Literature Review as input to Progress Report 1 (D1)

3000

WP2 Satellite image acquisitions plans WP3 Field data collection plans WP4 Health Safety and Environmental (HSE) plans WP5 Datasets for WPs 4000, 5000 and 6000

4000 WP6 Optimized algorithms in the form of documentation for Progress Review Reports (PRRs).



Work Package Item Deliverable WP7 Inputs to Progress Review Reports (PRRs)

5000 WP8 Report with documented results and demonstration of obtained results (Airbus)

6000 WP9 Report with documented results and demonstration of obtained results (Airbus)

7000 WP10 Lessons learned from the IDS Implementation

8000 WP11 License of IDS for Research Purposes WP12 Operational trial with O&G industry



3 Literature/Technology Review (WP 2000) It was the objective of this WP to review available literature on X-Band SAR, radar altimetry, and EO/IR technologies, with emphasis on ship detection and the discrimination, ship tracking and wake detection. Covering both radar and EO/IR sensors, the review covered a wide range of methodologies, including target detection, feature extraction, target decomposition, polarimetric techniques and data fusion. In addition, methods for the modelling of ship movements and approaches to vessel tracking were investigated. Based on the finding of this review, recommendations for subsequent WPs were formulated.

3.1 Target Detection Approaches Using SAR, EO/IR and Radar Altimeter Sensors

3.1.1 SAR Satellites

C-CORE (2013) conducted a comprehensive review of previous research on iceberg and ship detection and discrimination using SAR imagery. This review covered techniques and methodologies employed in research and operations, together with an evaluation of their respective performance and limitations. More recently, Nanette and Hannevik (2017) reviewed more than 73 publications in the fields of ship detection, iceberg detection, and ship and iceberg discrimination using single, dual-pol, quad-pol and compact polarimetric (CP) SAR data.

3.1.1.1 Relevant C-Band CP Technology

CP is a compromise between dual and quad polarization. In anticipation of the upcoming Radarsat Constellation Mission (RCM), the scientific community has begun to use simulated CP data from fully polarimetric RS-2 data. The Indian C-Band Radar Imaging Satellite 1 (RISAT-1) mission, active between 2012 and 2017, was also capable to acquire data in CP.

The results reported in Liu et al. (2010) demonstrate that a CP system provides better performance than a conventional dual-pol or single-pol systems. In addition, the swath width of CP, dual-pol and single-pol systems was twice that reported for quad-pol imagery of the same resolution. Detection performance was characterized by calculating receiver operating characteristics (ROC). The range of ROC curves demonstrated false alarm rate (FAR) up to 3 x 10-6.

The performance of the degree of polarization (DoP) in different hybrid/compact and linear dual-pol SAR modes in a ship-detection context was studied by Shirvany, Chabert, and Tourneret (2012). It was shown that the DoP provides valuable information for man-made object (ship and oil platforms) and oil-spill detection under different polarizations and incidence angles. Experimental results suggested that hybrid/compact and (HH, VV) dual-pol modes deliver better detection performance compared to conventional dual-pol modes, i.e., (HH, HV) and (VH, VV).

Atteia (2014) investigated the feasibility and potential benefits of using pseudo-quad data for improved ship detection. The pseudo-quad data are generated by an algorithm that aims to reconstruct some elements of the quad-pol covariance matrix from CP data specifically for maritime applications. The author also examined a new hybrid ship detection algorithm that utilized CP Stokes parameters and some of the derived parameters for ship detection. A pre-screening function merges Gaussian generalized



likelihood ratio test (GLRT), polarimetric GLRT and two-parameter constant false alarm rate (CFAR) to identify candidate ship objects. The subsequent discrimination algorithm uses a CP decomposition technique to discriminate ships from false alarms based on the type of scattering mechanism. Detection rates of 100% and 98% were reported for medium (50m) and low-resolution (100m) imagery, respectively.

(L. Xu et al. 2016) proposed a ship detection algorithm using the pulsed cosine transform (PCT) visual attention model. Polarimetric features, such as the relative phase and volume scattering component were extracted from m- decomposition of simulated CP data to eliminate false alarms and modify the PCT model. The CFAR algorithm based on a lognormal distribution was adopted to detect ship targets after a clutter distribution fitting procedure.

The ship detection performance of full quad-pol (QP) and full-pseudo-quad (PQ) data2 data were examined over 76 ships by Atteia and Collins (2013). The ship detection performance of the PQ HV data was the strongest of all the detectors, with a performance comparable to quad-pol data. Other relatively strong performers were HV and CTLR data. The performance was analyzed with the ROC curves demonstrating values of FAR up to 3 x 10-6.

Touzi, Hurley, and Vachon (2015) introduced scattered wave polarization signatures as a convenient graphical representation of the variations of the scattered wave rotation invariant parameters as a function of the transmitting antenna polarization. It was shown that the polarization signature of the degree of polarization and the total scattered intensity provide important information for enhanced characterization of ocean and ship scattering. The additional information provided by the maximum degree of polarization helps remove land target ambiguities.

A modified framework based on polarimetric features (phase difference, degree of circularity, correlation coefficient, double bounce scattering) for ship detection using CP SAR data was proposed by Fan et al. (2018). In particular, a guard-filter was used after feature extraction to reduce the effect of ocean clutter. Several simulated CP SAR images based on Gaofen-3 quad-polarization SAR data were used demonstrating better performance than the CFAR technique. The improvement in the ship detection performance of hybrid polarimetric SAR compared to dual-pol SAR was also recently demonstrated with the notch filter (Gao et al. 2018).

3.1.1.2 X-Band SAR Sensors

COSMOSkyMed (CSK) and TerraSAR-X (TSX) are operational X-Band satellite missions widely used for ship detection.

An automatic algorithm for iceberg detection from high resolution X-Band SAR single polarization data (HH of TSX) was developed by Frost, Ressel, and Lehner (2016). It is based on the iterative application of CFAR detector. In order to better discriminate icebergs from false alarms that frequently arise from rough

2 Several dual-pol configurations are suggested in the literature, including HV and PQ HV and the raw circular-transmit-linear-receive (CTLR).



seas a novel filter was added taking into account the outer shape of detected objects to filter recurring patterns, such as waves.

Velotto (2016) investigated ship detection using high resolution (1.2-6.6m) X-Band polarimetric SAR (PolSAR) imagery. The intention was also to research, develop and test advanced PolSAR techniques and algorithms able to overcome known issues of oil spill and ship detection using single-pol SAR data. It was demonstrated that metallic targets can be efficiently distinguished from the surrounding sea using high resolution X-Band dual-polarimetric SAR data. Both the HH–HV and the VV–VH polarimetric combinations can be used. The synergetic use of multi-frequency and multi-polarization satellite SAR data in the framework of maritime target detection has also been conducted. This research has led to the conclusions that the combined usage of TSX and Sentinel-1 data can improve the maritime surveillance capabilities since they carry different information regarding the target and ocean.

Tings and Velotto (2018) compared ship wake detectability in C-Band and X-Band using a large amount of SAR images (more than 300). The probability of detecting wakes in X-Band shows dependencies on vessel size and velocity as well as prevailing wind speed. In the C-Band, these dependencies are maintained, but with a general reduction in the correlation. This fact led authors to the conclusion that, for the available data set, ship wakes are more easily extracted from X-Band rather than C-Band imagery. This outcome is supported by a qualitative and quantitative analysis of a large data set of TSX and two independent C-Band sensors (i.e. Radarsat-2 and Sentinel-1).

3.1.1.3 SAR-Based Ship Detection

Ship detection using satellite data is a very active area of research. In the past ten years there were several publications which include a review of achievements in this area. Marino et al. (2015) and Marino, Dierking, and Wesche (2016) examined approaches that rely on the information kept in the spectrum of a single-look complex SAR image. Proposed sub-look detectors were applied and compared with well-known ship detection algorithms. The performance of the different sub-look algorithms was shown to be strongly dependent on polarization, frequency and resolution. These sub-look detectors are able to outperform the classical SAR intensity detector when the sea state is particularly high, leading to a strong clutter contribution. Approaches employing FFT to calculate spectrum of complex data were revealed to be beneficial to detect point-like targets, which are coherent targets.

Gierull (2017) provides a review of SAR based ship detection. In addition, the report proposed a novel statistical model and demonstrated that it is more accurate and robust than the commonly used K-distribution to model the SAR backscatter. The author also provides thorough theoretical analysis of sub-band cross-correlation techniques demonstrating that their performance does not improve detectability.

A novel technique based on the GLRT for ship detection in SAR imagery has been introduced by Iervolino and Guida (2017). Differently from the traditional CFAR algorithm, the GLRT approach is based also on the target distribution, which is modeled according to the geometric optics model described in Iervolino, Guida, and Whittaker (2016). The GLRT was applied to real datasets acquired from different sensors (TSX, Sentinel-1, and Airbus airborne demonstrator) operating at different bands (S, C, and X). The proposed approach greatly improves the target-to-clutter ratio (between 22 and 32 dB on average), detecting more



targets than the CFAR algorithm. However, its computational time is 1.5 times slower when compared to the CFAR algorithm.

Stasolla et al.(2015) conducted a comparison and performance assessment of four ship detection systems based on RS-2 and CSK imagery. The study demonstrated how systems can cope with challenging situations typical for the maritime environment. Five situations were identified, including 1) coastline, 2) structured sea clutter, 3) side lobes, 4) large targets and 5) ambiguities. The systems achieved 50%-90% probability of detection with the false alarm rates between 2.5 to 4.5E-08. None of the systems could consistently cope with all of the challenging situations. The detectors have different features and limitations, and where one detector fails the others were successful. Therefore, the overall results of the module could in principle be improved by properly merging the best aspects of the different ship detection reports. The benchmarking has helped these partners to focus on the detectors’ weaknesses.

Pan et al. (2017) report the rapid detection of vessels using Gaofen-3 (GF-3) SAR images. In a two-stage process, the iterative CFAR method is first employed to detect the potential ship pixels. In a second step, the mean-shift operation is applied on each potential ship pixel to identify the candidate target region.

Silva et al. (2011) describe the results of the JRC/Frontex joint spaceborne SAR maritime surveillance campaign carried out in Sardinia (Italy) and Palomares Canyon (Spain). The relatively low amount of data collected and analyzed does not allow making final conclusions about the feasibility of using SAR satellites for small boat detection. However, based on a more accommodating but reasonable analysis of the uncertainties in the trials, a detection score of 30-50% for the small boats is indicated, with calm sea conditions needed for positive detection.

3.2 EO/IR Sensors A variety of EO/IR satellites have been used for ship detection since 1978 (Kanjir, Greidanus, and Oštir 2018). The satellites used had varying resolutions ranging from 0.5m (VHR) to 250m and above. VHR satellites included IKONOS, QuickBird, GeoEye-1, WorldView-1, -2, Pleiades, Formosat-2, Gaofen-1. High and medium resolution satellites included Landsat-2, -5, -7, -8, Sentinel-2, RapidEye, CBERS, SPOT-2, -4, -5. Current low resolution satellite include MODIS on Aqua and Terra, VIIRS, Sentinel-3A, -3B.

3.2.1 Ship Detection Using High-Resolution EO/IR Sensors

An overview of existing literature on vessel/ship detection and classification from optical satellite imagery was recently conducted by Kanjir, Greidanus, and Oštir (2018). A total of 119 papers on optical vessel detection and classification were analyzed, spanning the period from 1978 to 2017. The following factors influencing the vessel detection accuracy were found to be most common:

Different weather conditions affecting sea surface characteristics; The quantity of clouds and haze; Solar angle; and Imaging sensor characteristics.



All these factors bring great variations in the selection of the most suitable method. Some factors still continue to pose unsolved challenges. The authors suggested that the algorithms for detection and classification should support a variety of targets and meteorological conditions, and ideally also a variety of optical satellite sensors and at least, they should be tested on many images under different conditions. The review of ship detection techniques highlighted eight groups of methods, including 1) threshold-based, 2) salient-based, 3) based on shape and texture, 4) statistical, 5) transform domain, 6) anomaly detection, 7) computer vision and 8) deep learning. The most commonly used methods were the first three groups.

A common flowchart of vessel detection, including preprocessing, detection and classification steps, is presented in Figure 3.

Figure 3. A common scheme of vessel detection workflow (Kanjir, Greidanus, and Oštir 2018).

The analysis of visual attention is considered a very important component in the human vision system for applications such as object detection. Saliency based detectors utilize local features to calculate a saliency map (Seo and Milanfar 2009). In general, saliency is defined as what drives human perceptual attention.

A method for ship detection from optical satellite images focusing on the extraction of ship candidates using texture statistics between sea and ships was described by Huang et al (2011). The prior knowledge of ship shapes is employed to remove the false ship candidates. Using a texture features operator (Zhu et al. 2010), the proposed method was shown insensitive to different waves, illumination changes, ships with different sizes and bright/dark intensities (see Figure 4).



Figure 4. Ship detection with strong ocean waves: Original SPOT 5 image (on the left) and ship detected with method described by Zhu et al. (2010) (on the right).

Dong, Liu, and Xu (2018) present a ship detection scheme to resolve major challenges for automatic ship detection in optical remote sensing images including cloud (see Figure 5), wave, island, wake clutters, and even the high variability of targets. The processing scheme comprises pre-screening and discrimination as the main coarse-to-fine stages. At the pre-screening stage, a visual saliency detection method is developed according to the difference of statistical characteristics between highly non-uniform regions which refer to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions significantly reducing false alarms.

Figure 5. Ship detection based on visual saliency model (Dong, Liu, and Xu 2018). Input VHR image (left), probability distributions (middle) for patches A-C, saliency model (right).

Corbane et al. (2010) describe an operational processing chain for ship detection using SPOT-5 optical satellite imagery. The proposed automatic detection model is based on statistical methods, mathematical morphology and other signal-processing techniques such as the wavelet analysis and Radon transform. The technique also includes cloud masking, using threshold information determined through histogram method, and contrast enhancement before automatic estimation of the threshold.

F. Xu et al. (2017) propose a hierarchical ship detection method to improve detection performance. At the ship detection stage, entropy information was used to construct a combined saliency model with self-adaptive weights to pre-screen ship candidates from across the entire maritime domain. To characterize ship targets and further reduce the false alarms, a novel descriptor based on gradient features was introduced. This descriptor is robust against clutter introduced by heavy clouds, islands, ship wakes as



well as the variation in target size. Yang et al. (2014) describe a ship detection method based on sea surface analysis. The method first analyzes whether the sea surface is homogeneous or not by using two new features. Then, a novel linear function combining pixel and region characteristics is employed to select ship candidates. Finally, Compactness (perimeter to area ratio) and length-width ratio are adopted to remove false alarms. Using SPOT 5 optical images with a pixel spacing of 5 meters for ship detection, a sliding window CFAR approach was adopted by Pagé, Proia, and Jubelin (2010). The intensity variations of the target and the background were modeled as white Gaussian noise.

A ship detection method based on the convolutional neural networks (CNNs), called specific CNN (SCNN), with input of specifically designed patterns (proposals) extracted from the ship model combined with an improved saliency detection method was developed by Zhang et al. (2016). Saliency feature (color and intensity) extraction aimed to achieve a similar function of a human eye observation system. The CNN introduces three ideas to reduce the number of parameters comparing to traditional neural networks (NN), including local receive fields, shared weights, and multiple convolution kernels. The CNN model is trained for ship detection from a large dataset and then the trained CNN model is tested on the ship proposals extracted by the ship models and the saliency map. A large set of high-resolution images, containing different kinds of ships, was collected. All positive images then were marked manually.

3.2.1.1 Ship Detection Using Medium-Resolution EO/IR Sensors

Abileah (2009) developed a semi-automated algorithm to detect ships in LANDSAT 7 images. The algorithm combines multispectral and pattern recognition methods to discriminate ships from ocean clutter. In clutter-free images (about 30% of all images) ships were easily detected as objects of one or more pixels with automated processing radiance levels that are significantly above the background. Only the visible bands (blue, green, and red) were used to make a reliable detection. For more cluttered images (i.e., with clouds, ocean waves, etc.) all the LANDSAT visible and IR bands (blue to mid-wave IR, and thermal IR) were required to discriminate ships from clutter. Pattern recognition methods were applied on the panchromatic image to further discriminate the target of six or more pixels. The algorithm is effective for clutter mitigation but at the expense of sacrificing some actual ships. In total, 54 images were processed to generate a statistical picture of ship traffic patterns.

The detection and classification of ships and icebergs in Sentinel-2 using thresholding was described by Heiselberg and Heiselberg (2017). The threshold values were adjusted depending on the presence of ice. Classification was performed by applying a decision criteria to reflectance, infrared and redness indexes. The authors were able to separate targets into seven classes, including ships, boats, islands, icebergs, wakes, grey ice and clouds (Figure 6) with overall detection accuracy 86%.



Figure 6. Sentinel-2 based detection over the capital Nuuk of Greenland (Heiselberg and Heiselberg 2017).

Wu et al. (2009) demonstrated that the mid infrared Bands 5 and 7 of Landsat Thematic Mapper (TM) were capable to effectively discriminate dredging ships from the surrounding turbid water in the lake. By contrast, while the visible and near infrared (NIR) Bands 1 to 4 were found to discriminate ships poorly. The results revealed that water turbidity influenced the detectability of a ship in the visible and near infrared bands, but not in the mid infrared bands.

The automated iceberg detection using Landsat 7 and 8 data was investigated by Scheick, Enderlin, and Hamilton (2018). A land mask was generated by applying a 100 m buffer seaward of the coastline. Cloud masks were created using classification based on machine learning algorithm applied to the top of atmosphere reflectance and brightness temperatures extracted for each classified pixel in the visible, NIR, short wave-infrared (SWIR), and thermal wavelengths. Reflectance values in the panchromatic band greater than the reflectance threshold equal to 0.19 were classified as iceberg. This threshold value was determined based on qualitative inspection of different thresholds across ten scenes and spanning both Landsat sensors. The sensitivity analysis was conducted to quantify the influence of uncertainty in the threshold value by running the algorithm for one of Landsat 8 scenes using higher and lower thresholds and compared the resulting iceberg delineations as well as the size distributions of icebergs to those derived using the reflectance threshold.

3.2.1.2 Ship Detection Using Low Resolution EO/IR Sensors

Maritime commercial ships also operate lights that can be detected from space. The Suomi National Polar-orbiting Partnership (S-NPP) satellite, which carries a Day/Night Band (DNB) radiometer, offers an improved ability for users to observe commercial shipping (see Figure 7) in remote areas such as the Arctic (Straka et al. 2015). Owing to S-NPP’s polar orbit and the DNB’s wide swath (~3040 km), the same location in Polar Regions can be observed for several successive passes via overlapping swaths—offering an ability to track ship motion.



Figure 7. DNB of S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) with icebreaker Taimyr on 18 Nov. 2013 (Straka et al. 2015).

Vachon et al. (2006) investigated meteorology and oceanography (MetOc) products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data for defence, search and rescue and environmental security operations. The results indicate a number of features which can be detected by MODIS, including aircraft contrails and ship tracks, water temperature, fronts and eddies, turbidity, freshwater plumes, etc. Rao et al. (2005) used satellite imagery from multiple missions to estimated ship speed and direction. Exhaust fumes creating observable streaks in the atmosphere helped identify the same ship in imagery from two different satellites. Ship velocities were estimated from the displacement of ships. Optical sensor data from MODIS and Ocean Color Monitor (India) were used to demonstrate this technique. More satellite observations can be used to continuously monitor the ship velocities. Kabatas et al. (2013) analyzed observations of ship tracks were analyzed for five geographically distributed cases using the MODIS cloud-product data. The ship tracks were detected because they were evident in the imagery of both Terra and Aqua MODIS data. Cloud-droplet growth with time was tracked in all of the ship-track plumes for the time difference of more than 6 hours.

3.2.2 Radar Altimeters

3.2.2.1 Current Radar Altimeters

A satellite based radar altimeter operates in nadir direction to measure distance between the satellite and earth surface. Altimeters are widely used for ocean applications to monitor waves, currents and other surface characteristics. There are seven radar altimetry missions currently in operation:

1. Jason-2; 2. Jason-3; 3. Saral/Altika; 4. Cryosat-2; 5. HY-2; 6. Sentinel-3A; and 7. Sentinel-3B.



3.1.4.1 Altimetry-Based Ship Detection

The capabilities of altimeters to detect icebergs in open water was first documented by Tournadre (2007) and Tournadre, Whitmer, and Girard-Ardhuin (2008). Gómez-Enri et al. (2016) and Tournadre (2014) have used altimetry data to detect and monitor ship traffic using echo waveform analysis. To this end, the archive of seven altimeter missions has been processed to create a to decade database of ship locations. The estimated annual density maps compare well with Automatic Identification System (AIS) observations.

The C-CORE team regularly uses an altimetry-based method for the detection of icebergs and recently developed ship-iceberg discrimination techniques (Zakharov et al. 2017). The probability of detecting icebergs larger than 150 m waterline-length was 47% in the Weddell Sea. The problem of discriminating ships and icebergs based on altimeter measurements was addressed using an ensemble of classifiers. A total of ten features were defined from the altimetry signal to be used as predictor variables in supervised classification. The classifier ensemble comprised discriminant functions, k-nearest neighbor, neural networks, support vector machines, and decision tree analysis. Several algorithms successfully classified objects as ships or icebergs with an accuracy exceeding 85%.

3.3 Sensor-Specific Feature Extraction

3.3.1 Classification of Vessel and Non-Vessel Targets

The automatic detection and classification of ships is challenging due to varying atmospheric conditions, the natural variability in the sea surface and the shape and size of the targets of interest. The extraction of vessels from satellite imagery typically follows a two-stage process of detection and classification. During the detection stage, candidate objects are identified against the background signature of the sea surface. During the classification stage, valid ship targets have to be discriminated against a variety of non-vessel targets, such as remaining ocean clutter, sidelobe effects, buoys, maritime infrastructure (e.g. wind turbines, offshore platforms), islets, rocks, sandbanks or coral reefs (Saur and Teutsch 2010).

(Zhu et al. 2010) divided all samples into typical ship and non-ship subclasses, including ships, clouds, ocean waves, islands and coastlines. Heiselberg and Heiselberg (2017) perform classification by applying specific decision criteria to reflectance, infrared and redness indexes. The authors were able to separate targets into seven classes, including ships, boats, islands, icebergs, wakes, grey ice and clouds. Kanjir, Greidanus, and Oštir (2018) classify detected vessels into different classes depending on size or type (i.e. military or non-military). The most popular classification method is based on a support vector machine (SVM) classifier. Other classifiers, such as NN, Bayesian classifier, random forest, etc. were also used.

Gallego, Pertusa, and Gil (2018) used a dataset composed of aerial and satellite optical imagery with more than 6000 samples to train and evaluate automatic ship and non-ship classification based on CNN architecture. The experimentation shows a success rate of over 99% for the CNN approach, in contrast with the 79% obtained with traditional methods in classification of ship images. A hierarchical ship classifier for CSK SAR data was introduced by Wang et al. (2014). A total of 41 ship chips were cut from the SAR images for later classification. After preprocessing of the ship chips to reduce side-lobe impact and correct ship orientation, geometric and backscattering characteristics of various ship types were analyzed. The ships were classified into bulk carriers, container ships, and oil tankers, with an accuracy of



93.3%, 80.0%, and 72.7%, respectively. Rainey, Reeder, and Corelli (2016) applied a CNN to classify ships into the respective classes of barge, cargo ship, container ship, and tanker. In addition, fast ship, small cloud and sun glint were included in the training data set as non-targets. The accuracy for ship and non-ship classification was 90% and for ship type the accuracy varied within 93-96%.

Bentes, Velotto, and Tings (2018) present SAR maritime target detection and classification using TSX high-resolution images. CNNs were cross-evaluated on a common data set composed of five maritime classes, including cargo, tanker, wind turbine, platform, and harbor structure. A CFAR algorithm was applied for ship detection. Based on experiments and tests, a multiple input resolution CNN model was proposed and its performance evaluated. The results indicate the combination of different input resolutions in the CNN model improves its ability to derive features, increasing the overall classification score.

3.3.1.1 SAR Feature Extraction and Target Decomposition

In a recent study, (Nanette and Hannevik 2017) proposed the following predictor variables to discriminate between ships and icebergs using dual-pol SAR imagery:

The Bayesian-based maximum likelihood-quadratic discriminant function; Brightness, texture, and shape as features that describe the targets; The HV/HH ratio; Target area, HH mean backscatter, and HV maximum backscatter; and Target morphology.

Ship and iceberg discrimination in quad-polarimetry data can rely on:

Polarimetric decomposition methods Pauli, Krogager, H/A/alpha and Yamaguchi; and Time-frequency polarimetric coherence analysis (Hu, Ferro-Famil, and Kuang 2013; C-CORE 2013).

The classifier proposed by Wang et al. (2014) is based on a decision tree approach applied to the mean and maximum backscatter coefficient and the calculated autocorrelation function. Theoretical analysis and evaluation of simulations to relate between the motions of the vessels and phase errors in their received SAR signals were conducted by Ontiveros (2016). Techniques for automatic extraction of features of the SAR signatures such as size, direction, range velocity component, and basic identification of the type of vessel were also proposed. Vessels are complex targets from the scattering perspective, and their signatures are expected to change as a function of the angle of observation. In addition, the dynamics of the vessels commonly introduce aberrations that may distort their SAR signature, complicating even further their analysis for real case scenarios. The two most common features for the classification of vessels in radar imaging are:

1. The characteristic reflectivity of the targets, i.e. the radar cross section selection, and 2. The shape and dimensions of the SAR signatures associated with them.

Wang (2014) conducted feature analysis for ship detection in high-resolution SAR imagery. A fast block detector was designed to extract sea clutter in a uniform local area, and then a CFAR detector was



employed. Ship features, including the kernel density estimation of ships, the aspect ratio, and ship pixels, were analyzed to construct feature confidence, which is a normalized index to show the possibility of a potential target to be a ship. Schwegmann, Kleynhans, and Salmon (2017) propose a three-stage method. The low-threshold CFAR pre-screening method identifies likely ship candidates. This is followed by Haar-like feature extraction which is then fed to an adaptable cascade classifier using adaptive boosting (AdaBoost) to discriminate ships from non-ships. He et al. (2018) applied an adaptive sliding window followed by backscatter and texture features to train a SVM. Texture features were based on Gray-level co-occurrence Matrix (GLCM), including angular second moment, entropy, inverse difference moment and correlation. According to Ouchi (n.d.), possible SAR features for ship classification include the following parameters:

Length, width; Moments of inertia; Fractal measures; Wavelet coefficient; Target and shadow contour; Image itself; Energy in brightest pixels; Polarimetric channel ratio; Attributed scattering center; Probability density function; and Topographic features.

Multi-polarization parameters (from H/A/alpha decomposition, span, and the amplitude (magnitude) and phase elements of the polarimetric covariance matrix) were used to characterize different scattering behaviours of the ship target and sea clutter by Xing et al (2013). The study proposes a feature selection and weighted support vector machine classifier-based algorithm to detect ships in PolSAR imagery. First, the method constructs a feature vector that consists of multi-polarization parameters. Then, different polarization parameters are refined and weighted according to their significance in the SVM classifier. Finally, ships are classified from the sea background and other false alarms.

Leng et al. (2016) divided SAR features for ship/non-ship discrimination into four categories, including texture, size and shape (length, width and aspect ratio), contrast and polarimetric features. Textural features, such as occurrence, skewness, entropy, variance, mean and data range; and co-occurrence: correlation, second moment, entropy, dissimilarity, contrast, homogeneity, variance and mean over ship targets were analyzed by Khesali et al. (2015). Optimized results were observed with texture analysis in single-pol SAR images, such as Sentinel-1.

3.3.1.2 Feature Extraction in EO/IR

Kanjir, Greidanus, and Oštir (2018) report that most authors performed discrimination based on simple geometrical features (the length and the width) or their combinations. Some authors used the combination of geometrical features and spectral signatures. Among other features there were shape, texture, similarity, confidence map, different gradient orientation, Dempster-Shafer evidence theory,



shape, elongation, context information, phase spectrum of quaternion Fourier transform, Radon transform, histogram of oriented gradients and gradient features. In addition to the shape and appearance of ships, the spatial relations of ship regions namely topology structure characterize the symmetry of the sides of ships and provi

A detection algorithm based on saliency segmentation and extraction of the local binary pattern (LBP) descriptor combined with ship structure was described by Yang et al. (2017). A saliency segmentation

surfaces was presented. Considering the distinct patterns of ships (i.e., the boundary between an object and its background), a pattern distinctness measure based on the phase spectrum of a Fourier transform is applied. Intensity distinctness and pattern distinctness and their combination contribute in discriminating ship candidates. Then, simple shape analysis is adopted to eliminate obvious false targets. Finally, a structure-LBP (local binary pattern) feature that characterizes the inherent topology structure of ships is applied to discriminate true ship targets. Area, length-width ratio and compactness features were also considered.

Tang et al. (2015) used a Deep Neural Network (DNN) for hierarchical ship feature extraction in the wavelet domain providing more robust performance under variant conditions. After extraction of several connected regions from the detection masks, the geometric properties (area, major minor axis ratio, compactness) of the connected regions were used to locate the ship candidates. DNN with decomposing inputs into multiple nonlinear processing layers, achieves better performance with much less parameters in each layer than other techniques. Wavelet singularities of low frequency are detected to train a stacked denoising autoencoder (SDA). The weight matrices of the trained SDAs are considered as feature extractors for low- and high-frequency sub-bands, respectively.

3.3.1.3 Feature Extraction in Altimetry Data

Zakharov et al. (2017) used satellite altimetry to for ship/iceberg detection and discrimination. Each detected target in altimetry data was described by the following five parameters (features) describing the backscatter and shape of altimetry signatures for the Ku- and C- Bands:

Maximum backscatter coefficient; Total backscattered power; Number of samples per target; Number of parabolas per target; and Number of specular reflectors in parabolas.

A total of ten features (five for each band) were statistically analyzed to understand their contribution to discriminating between ships and icebergs. All features were analyzed as predictor variables in classification algorithms.



3.4 Image-Based Ship and Iceberg Tracking

3.4.1 Ship Tracking

Yao et al. (2018) propose a ship-tracking method using sequential imagery acquired by GF-4, a geostationary optical satellite with a spatial resolution of 50 meters. A local visual saliency map based on local peak signal-to-noise ratio is used to detect ships in a single frame of GF-4 satellite sequential images. A NIR band was used for ship detection. The accurate positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients and AIS data of ships.

A coastal-based automatic maritime surveillance system was presented by Bloisi et al. (2011). Boat detection is performed by means of a Haar-like classifier in order to obtain robustness with respect to targets having very different size, reflections and wakes on the water surface, and apparently motionless boats anchored off the coast.

Liu, Peng, and Chang (1997) employed wavelet analysis for the automated detection and tracking of features using satellite imagery. The wavelet transform was applied to the satellite images, such as SAR, Advanced Very-High-Resolution Radiometer (AVHRR), and Coastal Zone Color Scanner (CZCS) for feature extraction. The evolution of mesoscale features such as oil slicks, fronts, eddies, and ship wakes can be tracked by the wavelet analysis using satellite data from repeating paths. Meng and Kerekes (2012) developed an object tracking algorithm that includes moving object estimation, target modeling, and target matching three-step processing. Potentially moving objects are first identified on the time-series images. The target is then modeled by extracting both spectral and spatial features. In the target matching procedure, the Bhattacharyya distance, histogram intersection, and pixel count similarity are combined in a novel regional operator design. The algorithm has been tested using a set of multi-angular sequence images acquired by the WorldView-2 satellite.

3.4.2 Iceberg Tracking

Larssen (2015) proposed an algorithm for automatic tracking of targets in satellite images. The algorithm has three stages: detection of targets, matching targets in the image with targets from previous images, and classification of targets. The matching is performed using a geometrical shape representation rendered for each target, and the classification feature uses a set speed limit to distinguish between vessel and ice targets. The algorithm’s performance was assessed by applying it to a set of RS-2 images covering the East Coast of Greenland. In addition to a multi-polarized information, an indication of target types was found by comparison of motion patterns of free-floating ice targets with human-controlled vessels.

The analysis of the drifting path of one of the two giant icebergs collapsed from the Nansen Ice Shelf (in Antarctica) on April 7, 2016 was performed by Moctezuma-Flores and Parmiggiani (2017). The study was carried out using Sentinel-1 satellite images. Six SAR images captured a month after the collapse were used. A processing scheme was implemented which consists of the following steps: (1) speckle filtering, (2) binary segmentation, and (3) iceberg centroid detection. The final result is the tracking of the iceberg, with its relative velocity, at different time intervals.



A computer-based technique was proposed by Silva and Bigg (2005) to identify icebergs allowing the shape of small to medium icebergs to be retained and used for tracking between images acquired at different times. The identification consists of (1) automatic segmentation, followed by (2) classification of these objects as icebergs or non-icebergs. The classification stage requires user intervention as the icebergs are not always a separable class from other objects, most notably sea ice. The icebergs are then matched between images using their size and shape similarities, and their position tracked. The system performance was assessed by applying the technique to three wintertime ERS-1 images (on the Eastern Weddell Sea coast). In order to perform tracking, objects previously identified as icebergs must be matched between images acquired at different times, and possibly different locations. This was done by ranking iceberg pairs in terms of size similarities followed by a finer ranking in terms of shape resemblance. For each image, an iceberg database, containing the same object parameters (mean backscatter, area, major/minor axis, perimeter/area) already used for iceberg identification, is produced. The results show both the identification and the tracking to be effective. Icebergs with sizes of 200 m to 10 km across in two areas of the Weddell Sea were tracked using ERS-1/2 SAR satellites (Gladstone and Bigg 2002). Iceberg speeds and fluxes (number of icebergs and their total surface area) were calculated from the SAR images using the following steps:

1. For each region of interest a time series of 8 or 9 images was obtained at three (or occasionally six) day intervals.

2. In each image, icebergs were identified manually using the criteria of backscatter homogeneity and edge contrasts. The following properties were measured for each iceberg: length (greatest horizontal extent), perimeter, above water surface area, and position.

3. An algorithm was then used to identify, for each iceberg in an image, possible matches for that iceberg in the next image in the time series. The program simply selected all the icebergs in the next image whose length, perimeter and surface area were all within 10% of those properties of the iceberg in the original image. The algorithm also selected, as possible grounded matches, any icebergs in the next image whose position was within 250 m of an original iceberg’s position.

4. These possible moving or grounded matches were then confirmed or rejected by a visual inspection.

Williams et al. (1999) describe an image analysis technique developed to identify icebergs in SAR images of Antarctica and to determine the outlines of these icebergs. The technique used a pixel bonding process to delineate the edges of the icebergs. It then separated them from the background water and sea ice by an edge-guided image segmentation process. Characteristics such as centroid position and iceberg area were calculated for each iceberg. The technique has been tested on three ERS-1 SAR sub-images in which it succeeded in identifying virtually all segments containing icebergs of size six pixels or larger. The images were first passed through an averaging filter to reduce speckle.

3.5 Recommendations for DIOS The usage of different space borne sensors for ship detection requires implementation of detection techniques for each sensor.



3.5.1.1 Detection of Ships in SAR Imagery

SAR imagery is routinely used to detect ships with minimum size comparable to the sensor resolution. The ship detection in SAR imagery based on a CFAR algorithm is a well-established technology. The major goal of using X-Band SAR data in this project is to evaluate capabilities of detecting small ships and ships in ports or near the coast. It would be interesting to identify sea conditions suitable for positive detection of small ships. SAR data will be used for validation of the results detected with altimetry and EO/IR data.

3.5.1.2 Detection of Ships in Altimetry Data

In this project, marine targets (ships and icebergs) will be detected by Jason-2 (JS2) and Jason-3 (JS3) altimeters. This research will use C-CORE’s Altimeter Iceberg Detector (AID) software, which is based on detecting iceberg signature using a Fourier phase correlation method and pattern recognition algorithms. AID is regularly used to detect iceberg locations for navigation support during global yacht races. In this study, AID will be used to collect training and validation data (Zakharov et al. 2017) in areas near latitude of 66 N degrees. Most satellite altimeters have dual-frequency capacity using the Ku- and C-Bands of electromagnetic spectrum. In order to improve the quantitative discrimination between ships and icebergs the concurrent use of the Ku and C-Band altimetry signals will be applied.

3.5.1.3 Detection of Ships in EO/IR Data

It can be useful to implement published detection algorithms that follow a common framework (Figure 3) for various optical sensors and can support different spectral bands and to test algorithms on various sea conditions and ship types. It is important to evaluate algorithm detection performance using detection ratio and false alarm rate. The algorithms for detection and classification should support a variety of targets and meteorological conditions, and ideally also a variety of optical satellite sensors and they should be tested on many images under different environmental conditions.

3.5.1.4 Discrimination of Ship from Non-Ship Targets

It is recommended to use an ensemble of classifiers to discriminate ship from non-ship targets. The main problems with detection and classification are:

Complex sea surface (waves, sunlight, clouds and small islands) contributes to false detections. The variation of the reflectivity of vessel targets resulting in lower grey levels than the sea surface background. It is difficult to distinguish the vessel from its wake in most optical images. Small vessels are hard to classify. Small amount of data for training and validation does not contribute to algorithm robustness.

The previous work on implementation of various classifiers to altimetry and full polarimetric RS-2 data demonstrated high performances of SVM and combined classifiers.



3.5.1.5 Summary of Recommendations

The recommendations described above are summarized in Table 4. It is expected that the developed algorithms would have executable time suitable for operational tasks.

Table 4. List of Recommendations

Recommendation Scope of work Expected performance 1. Ship detection in the SAR image. Use current CFAR-based detection algorithm. Detection accuracy is

above 90% 2. Ship detection using EO/IR data.

Implement algorithms (e.g. thresholding) found in the literature for EO/IR spaceborne data.

Detection accuracy is above 90%

3. Ship and iceberg detection using altimetry data. Use current AID software. Detection accuracy is

about 50% 4. Ship and non-ship discrimination. Use SVM, NN and ensemble of classifiers. Classification accuracy is

above 80%

5. Ship detection close to land. Semi-automatic ship detection close to land, in harbors and ports.

Detection accuracy is above 90%

3.6 DIOS Project Results Relevant to DND and CAF It is anticipated that the final results from the DIOS project will be quite applicable and provide recommendations to several sections of the DND and CAF Space-Based Surveillance Requirements Report, contained in Appendix A. The DIOS results will contribute to the following items in this report.

Algorithm development has to produce results capable to satisfy Space-Based Surveillance (SBS) Requirements (DND 2017), such as:

[Req 100.1] Multi-Role Operational Surveillance Capability. The SBS system could comprise of SAR, AIS, EO/IR, HSI, Signals Intelligence and other complementary sensors. [Req 202.2] Ship Detection Close to Land. The SBS system is required to have semi-automatic ship detection close to land, in harbors and ports. [Req 204.1] Vessel Detection Parameters. The SBS system is required to automatically detect vessels of 15 meters length and larger with a 90% probability of detection and a very low false alarm rate (less than 2.5(10)-9), in all weather conditions up to and including sea state 5 Beaufort Wind Scale 6 (wave height 3 to 4m), for all of the Maritime Areas defined in Annexes A and B of (DND 2017) [Req 204.3] Detection Performance in Ice. The SBS system is required to have the same automatic ship detection performance as stated above, even for areas where icebergs are present and where ships may be breaking ice.



4 Future Work The next steps in the DIOS project include the start of WP 3000, Data Collection and Validation. This task will involve the collection and validation of multi-satellite data ship-like targets (e.g. ocean surface features, iceberg, and vessels) in St. John’s and Halifax. Data collection for icebergs will include potential field campaigns staged in collaboration with other C-CORE projects or opportunistic field work with the IIP or FedNav. The immediate tasks will include the following:

A collection of very high resolution and medium resolution EO/IR and X-Band SAR data will be conducted over ports of St. John’s and Halifax. During satellite data collection the C-CORE analysts will gather validation information by taking field photographs and using webcams in ports. Performance of low resolution EO/IR sensors can be demonstrated with ship voyages of FedNav (previous or new). The X-Band data acquired by TSX satellite and C-Band RS-2 data can also be used for validation. Freely available altimetry data acquired by Jason-2/3 altimeters will be obtained and analyzed over Baffin Bay and the Labrador Sea. The TSX and RS-2 data can also be used for validation.



5 References Abileah, R. 2009. “Surveying Coastal Ship Traffic with LANDSAT.” In OCEANS 2009, 1–6.

https://doi.org/10.23919/OCEANS.2009.5422109. Atteia, Allah. 2014. “On the Use of Hybrid Compact Polarimetric SAR for Ship Detection.” PhD Thesis,

University of Calgary. Atteia, G.E., and Michael J. Collins. 2013. “On the Use of Compact Polarimetry SAR for Ship Detection.”

ISPRS Journal of Photogrammetry and Remote Sensing 80 (June): 1–9. https://doi.org/10.1016/j.isprsjprs.2013.01.009.

Bentes, Carlos, Domenico Velotto, and Bjorn Tings. 2018. “Ship Classification in TerraSAR-X Images With Convolutional Neural Networks.” IEEE Journal of Oceanic Engineering 43 (1): 258–66. https://doi.org/10.1109/JOE.2017.2767106.

Bloisi, Domenico, Luca Iocchi, Michele Fiorini, and Giovanni Graziano. 2011. “Automatic Maritime Surveillance with Visual Target Detection.” In Proc. of the International Defense and Homeland Security Simulation Workshop (DHSS), 141–145.

C-CORE. 2013. “Summary of Previous Research in Iceberg and Ship Detection and Discrimination in SAR.” Report R-13-060-1098. Revision 4.0. C-CORE, Canada. http://publications.gc.ca/collections/collection_2016/rddc-drdc/D68-3-C79-2014-eng.pdf.

Corbane, Christina, Laurent Najman, Emilien Pecoul, Laurent Demagistri, and Michel Petit. 2010. “A Complete Processing Chain for Ship Detection Using Optical Satellite Imagery.” International Journal of Remote Sensing 31 (22): 5837–54. https://doi.org/10.1080/01431161.2010.512310.

DND. 2017. “Department of National Defence and Canadian Armed Forces Space-Based Surveillance Requirements. DND SBS DRD - V1.0.”

Dong, Chao, Jinghong Liu, and Fang Xu. 2018. “Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor.” Remote Sensing 10 (3): 400. https://doi.org/10.3390/rs10030400.

Fan, Qiancong, Feng Chen, Ming Cheng, Cheng Wang, and Jonathan Li. 2018. “A Modified Framework for Ship Detection from Compact Polarization SAR Images.” In . Valencia, Spain.

Frost, Anja, Rudolf Ressel, and Susanne Lehner. 2016. “Automated Iceberg Detection Using High-Resolution X-Band SAR Images.” Canadian Journal of Remote Sensing 42 (4): 354–66. https://doi.org/10.1080/07038992.2016.1177451.

Gallego, Antonio-Javier, Antonio Pertusa, and Pablo Gil. 2018. “Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks.” Remote Sensing 10 (4): 511. https://doi.org/10.3390/rs10040511.

Gao, Gui, Sheng Gao, Juan He, and Gaosheng Li. 2018. “Ship Detection Using Compact Polarimetric SAR Based on the Notch Filter.” IEEE Transactions on Geoscience and Remote Sensing 56 (9): 5380–93. https://doi.org/10.1109/TGRS.2018.2815582.

Gierull, Christoph. 2017. “On the Statistical Examination of Prevailing Ship Detection Methodologies for Space-Based Synthetic Aperture Radar Imagery.” DRDC-RDDC-2017-R024. Ottawa, ON, Canada.

Gladstone, R., and G.R. Bigg. 2002. “Satellite Tracking of Icebergs in the Weddell Sea.” Antarctic Science 14 (03). https://doi.org/10.1017/S0954102002000032.

Gómez-Enri, Jesús, Andrea Scozzari, Francesco Soldovieri, Josep Coca, and Stefano Vignudelli. 2016. “Detection and Characterization of Ship Targets Using CryoSat-2 Altimeter Waveforms.” Remote Sensing 8 (3): 193. https://doi.org/10.3390/rs8030193.



He, Guangjun, Zhenghuan Xia, Hao Chen, Ke Li, Zhilong Zhao, Yuhua Guo, and Pengming Feng. 2018. “An Adaptive Ship Detection Algorithm for HRWS SAR Images Under Complez Background: Application to Sentinel-1 Data.” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 42 (3).

Heiselberg, Peder, and Henning Heiselberg. 2017. “Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification.” Remote Sensing 9 (11): 1156. https://doi.org/10.3390/rs9111156.

Hu, Canbin, Laurent Ferro-Famil, and Gangyao Kuang. 2013. “Multi-Dimensional Coherent Time-Frequency Analysis for Ship Detection in Polsar Imagery.” In 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 101–4. Melbourne, Australia: IEEE. https://doi.org/10.1109/IGARSS.2013.6721102.

Huang, Gaopan, Yanqing Wang, Yushuang Zhang, and Yuan Tian. 2011. “Ship Detection Using Texture Statistics from Optical Satellite Images.” In 2011 International Conference on Digital Image Computing: Techniques and Applications, 507–12. Noosa, QLD, Australia: IEEE. https://doi.org/10.1109/DICTA.2011.91.

Iervolino, Pasquale, and Raffaella Guida. 2017. “A Novel Ship Detector Based on the Generalized-Likelihood Ratio Test for SAR Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (8): 3616–30. https://doi.org/10.1109/JSTARS.2017.2692820.

Iervolino, Pasquale, Raffaella Guida, and Philip Whittaker. 2016. “A Model for the Backscattering From a Canonical Ship in SAR Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (3): 1163–75. https://doi.org/10.1109/JSTARS.2015.2443557.

Kabatas, Burcu, W. Paul Menzel, Ata Bilgili, and Liam E. Gumley. 2013. “Comparing Ship-Track Droplet Sizes Inferred from Terra and Aqua MODIS Data.” Journal of Applied Meteorology and Climatology 52 (1): 230–41. https://doi.org/10.1175/JAMC-D-11-0232.1.

Kanjir, Urška, Harm Greidanus, and Krištof Oštir. 2018. “Vessel Detection and Classification from Spaceborne Optical Images: A Literature Survey.” Remote Sensing of Environment 207 (March): 1–26. https://doi.org/10.1016/j.rse.2017.12.033.

Khesali, E, H Enayati, M Modiri, and M Mohseni Aref. 2015. “Automatic Ship Detection in Single-Pol SAR Images Using Texture Features in Artificial Neural Networks.” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40 (1): 395.

Larssen, Guro. 2015. “Ice Detection and Tracking Based on Satellite and Radar Images.” Master’s Thesis, Norway: NTNU-Norwegian University of Science and Technology.

Leng, Xiangguang, Kefeng Ji, Shilin Zhou, Xiangwei Xing, and Huanxin Zou. 2016. “An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery.” Sensors 16 (9): 1345. https://doi.org/10.3390/s16091345.

Liu, A.K., C.Y. Peng, and S.Y.-S. Chang. 1997. “Wavelet Analysis of Satellite Images for Coastal Watch.” IEEE Journal of Oceanic Engineering 22 (1): 9–17. https://doi.org/10.1109/48.557535.

Liu, Chen, Paris W. Vachon, Ryan A. English, and Nicholas Sandirasegaram. 2010. “Ship Detection Using RADARSAT-2 Fine Quad Mode and Simulated Compact Polarimetry Data.” Defence Research and Development Canada.

Marino, A., W. Dierking, and C. Wesche. 2016. “A Depolarization Ratio Anomaly Detector to Identify Icebergs in Sea Ice Using Dual-Polarization SAR Images.” IEEE Transactions on Geoscience and Remote Sensing 54 (9): 5602–15. https://doi.org/10.1109/TGRS.2016.2569450.



Marino, A., R. Rulli, C. Wesche, and I. Hajnsek. 2015. “A New Algorithm for Iceberg Detection with Dual-Polarimetric SAR Data.” In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3446–49. https://doi.org/10.1109/IGARSS.2015.7326561.

Meng, Lingfei, and John P. Kerekes. 2012. “Object Tracking Using High Resolution Satellite Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (1): 146–52. https://doi.org/10.1109/JSTARS.2011.2179639.

Moctezuma-Flores, M., and F. Parmiggiani. 2017. “Tracking of the Iceberg Created by the Nansen Ice Shelf Collapse.” International Journal of Remote Sensing 38 (5): 1224–34. https://doi.org/10.1080/01431161.2016.1275054.

Nanette, Tonje, and Arnesen Hannevik. 2017. “Literature Review on Ship and Ice Discrimination.” FFI-RAPPORT 17/16310. Norwegian Defence Research Establishment (FFI.

Ontiveros, Yam. 2016. “Simulation, Detection, and Classification of Vessels in Maritime SAR Images.” Universitat Politecnica de Catalunya, Spain.

Ouchi, Kazuo. n.d. “Introduction of Ship Detection Technology Using SAR and Trend of Ship Classification.” National Defense Academy, Japan. Accessed January 11, 2018. http://www.jsforum.or.jp/stableuse/2017/pdf/17_Professor%20Kazuo%20Ouchi%20(PhD).pdf.

Pagé, Vincent, Nadia Proia, and Guillaume Jubelin. 2010. “Maritime Surveillance with the Use of Optical Satellite Images.” Proc. OCOSS.

Pan, Zongxu, Lei Liu, Xiaolan Qiu, and Bin Lei. 2017. “Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode.” Sensors 17 (7): 1578. https://doi.org/10.3390/s17071578.

Rainey, Katie, John D Reeder, and Alexander G Corelli. 2016. “Convolution Neural Networks for Ship Type Recognition.” In Automatic Target Recognition XXVI, 9844:984409. International Society for Optics and Photonics.

Saur, Günter, and Michael Teutsch. 2010. “SAR Signature Analysis for TerraSAR-X-Based Ship Monitoring.” In , edited by Lorenzo Bruzzone, 78301O. Toulouse, France. https://doi.org/10.1117/12.866836.

Scheick, Jessica, Ellyn M. Enderlin, and Gordon Hamilton. 2018. “Automated Iceberg Detection Using Landsat: Method and Example Application in Disko Bay, West Greenland.” The Cryosphere Discussions, May, 1–21. https://doi.org/10.5194/tc-2018-73.

Schwegmann, C. P., W. Kleynhans, and B. P. Salmon. 2017. “Synthetic Aperture Radar Ship Detection Using Haar-Like Features.” IEEE Geoscience and Remote Sensing Letters 14 (2): 154–58. https://doi.org/10.1109/LGRS.2016.2631638.

Seo, Hae Jong, and Peyman Milanfar. 2009. “Nonparametric Bottom-up Saliency Detection by Self-Resemblance.” In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 45–52. Miami, FL: IEEE. https://doi.org/10.1109/CVPRW.2009.5204207.

Shirvany, Reza, Marie Chabert, and Jean-Yves Tourneret. 2012. “Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (3): 885–92. https://doi.org/10.1109/JSTARS.2012.2182760.

Silva, Tiago A.M., and Grant R. Bigg. 2005. “Computer-Based Identification and Tracking of Antarctic Icebergs in SAR Images.” Remote Sensing of Environment 94 (3): 287–97. https://doi.org/10.1016/j.rse.2004.10.002.

Silva, Victor M. G, Harm Greidanus, European Commission, Joint Research Centre, and Institute for the Protection and the Security of the Citizen. 2011. JRC-Frontex Spaceborne SAR Small Boat Detection Campaign - Italy & Spain: Results of the Spaceborne SAR Small Boat Detection Campaign Carried



out by the EC-JRC in Cooperation with Frontex in Sardinia-Italy and Palomares Canyon - Spain in September and October 2009, Respectively. Luxembourg: Publications Office. http://dx.publications.europa.eu/10.2788/1727.

Srinivasa Rao, N., M.M. Ali, M.V. Rao, and I.V. Ramana. 2005. “Estimation of Ship Velocities From MODIS and OCM.” IEEE Geoscience and Remote Sensing Letters 2 (4): 437–39. https://doi.org/10.1109/LGRS.2005.853572.

Stasolla, Mattia, Carlos Santamaria, Jordi J. Mallorqui, Gerard Margarit, and Nick Walker. 2015. “Automatic Ship Detection in SAR Satellite Images: Performance Assessment.” In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2473–76. Milan, Italy: IEEE. https://doi.org/10.1109/IGARSS.2015.7326311.

Straka, William, Curtis Seaman, Kimberly Baugh, Kathleen Cole, Eric Stevens, and Steven Miller. 2015. “Utilization of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band for Arctic Ship Tracking and Fisheries Management.” Remote Sensing 7 (1): 971–89. https://doi.org/10.3390/rs70100971.

Tang, Jiexiong, Chenwei Deng, Guang-Bin Huang, and Baojun Zhao. 2015. “Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine.” IEEE Transactions on Geoscience and Remote Sensing 53 (3): 1174–85. https://doi.org/10.1109/TGRS.2014.2335751.

Tings, Björn, and Domenico Velotto. 2018. “Comparison of Ship Wake Detectability on C-Band and X-Band SAR.” International Journal of Remote Sensing 39 (13): 4451–68. https://doi.org/10.1080/01431161.2018.1425568.

Tournadre, Jean. 2007. “Signature of Lighthouses, Ships, and Small Islands in Altimeter Waveforms.” Journal of Atmospheric and Oceanic Technology 24 (6): 1143–1149.

———. 2014. “Anthropogenic Pressure on the Open Ocean: The Growth of Ship Traffic Revealed by Altimeter Data Analysis.” Geophysical Research Letters 41 (22): 7924–32. https://doi.org/10.1002/2014GL061786.

Tournadre, Jean, Kirk Whitmer, and Fanny Girard-Ardhuin. 2008. “Iceberg Detection in Open Water by Altimeter Waveform Analysis.” Journal of Geophysical Research: Oceans 113 (C8).

Touzi, Ridha, Jeff Hurley, and Paris W. Vachon. 2015. “Optimization of the Degree of Polarization for Enhanced Ship Detection Using Polarimetric RADARSAT-2.” IEEE Transactions on Geoscience and Remote Sensing 53 (10): 5403–24. https://doi.org/10.1109/TGRS.2015.2422134.

Vachon, Paris W., B.G. Whitehouse, W.M. Renaud, Roger De Abreu, and D. Billard. 2006. “Polar Epsilon MODIS and Fused MODIS/RADARSAT MetOc Products for National Defence and Domestic Security.” DRDC-OTTAWA-TM-2006-067 — Technical Memorandum. Defence Research and Development Canada Ottawa.

Velotto, Domenico. 2016. “Oil Spill and Ship Detection Using High Resolution Polarimetric X-Band SAR Data.” PhD Thesis, Technische Universität München.

Wang, Chao, Shaofeng Jiang, Hong Zhang, Fan Wu, and Bo Zhang. 2014. “Ship Detection for High-Resolution SAR Images Based on Feature Analysis.” IEEE Geoscience and Remote Sensing Letters 11 (1): 119–23. https://doi.org/10.1109/LGRS.2013.2248118.

Wang, Chao, Hong Zhang, Fan Wu, Shaofeng Jiang, Bo Zhang, and Yixian Tang. 2014. “A Novel Hierarchical Ship Classifier for COSMO-SkyMed SAR Data.” IEEE Geoscience and Remote Sensing Letters 11 (2): 484–88. https://doi.org/10.1109/LGRS.2013.2268875.



Williams, R. N., W. G. Rees, and N. W. Young. 1999. “A Technique for the Identification and Analysis of Icebergs in Synthetic Aperture Radar Images of Antarctica.” International Journal of Remote Sensing 20 (15–16): 3183–99. https://doi.org/10.1080/014311699211697.

Wu, Guofeng, Jan de Leeuw, Andrew K. Skidmore, Yaolin Liu, and Herbert H.T. Prins. 2009. “Performance of Landsat TM in Ship Detection in Turbid Waters.” International Journal of Applied Earth Observation and Geoinformation 11 (1): 54–61. https://doi.org/10.1016/j.jag.2008.07.001.

Xing, Xiangwei, Kefeng Ji, Huanxin Zou, and Jixiang Sun. 2013. “Feature Selection and Weighted SVM Classifier-Based Ship Detection in PolSAR Imagery.” International Journal of Remote Sensing 34 (22): 7925–44. https://doi.org/10.1080/01431161.2013.827812.

Xu, Fang, Jinghong Liu, Mingchao Sun, Dongdong Zeng, and Xuan Wang. 2017. “A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery.” Remote Sensing 9 (3): 280. https://doi.org/10.3390/rs9030280.

Xu, Lu, Hong Zhang, Chao Wang, Bo Zhang, and Sirui Tian. 2016. “Compact Polarimetric SAR Ship Detection with M- Remote Sensing 8 (9): 751. https://doi.org/10.3390/rs8090751.

Yang, Feng, Qizhi Xu, and Bo Li. 2017. “Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature.” IEEE Geoscience and Remote Sensing Letters 14 (5): 602–6. https://doi.org/10.1109/LGRS.2017.2664118.

Yang, G., B. Li, S. Ji, F. Gao, and Q. Xu. 2014. “Ship Detection From Optical Satellite Images Based on Sea Surface Analysis.” IEEE Geoscience and Remote Sensing Letters 11 (3): 641–45. https://doi.org/10.1109/LGRS.2013.2273552.

Yao, Libo, Yong Liu, and You He. 2018. “A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images.” Sensors 18 (7): 2007. https://doi.org/10.3390/s18072007.

Zakharov, Igor, Paul Adlakha, Thomas Puestow, Desmond Power, Sherry Warren, and Mark Howell. 2016. “Monitoring Pipeline Rights of Way Using Optical Satellite Imagery.” In 11th Pipeline Technology Conference 2016. Berlin, Germany: EITEP Institute.

Zakharov, Igor, Thomas Puestow, Andrew Fleming, Janaka Deepakumara, and Desmond Power. 2017. “Detection and Discrimination of Icebergs and Ships Using Satellite Altimetry.” In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 882–85. https://doi.org/10.1109/IGARSS.2017.8127093.

Zhang, Rong, Jian Yao, Kao Zhang, Chen Hua Feng, and Jiadong Zhang. 2016. “S-Cnn-Based Ship Detection from High-Resolution Remote Sensing Images.” In .

Zhu, Changren, Hui Zhou, Runsheng Wang, and Jun Guo. 2010. “A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features.” IEEE Transactions on Geoscience and Remote Sensing 48 (9): 3446–56. https://doi.org/10.1109/TGRS.2010.2046330.



Appendix A: DND and CAF Report


LAST PAGE OF DOCUMENT

DOCUMENT CONTROL DATA *Security markings for the title, authors, abstract and keywords must be entered when the document is sensitive

1. ORIGINATOR (Name and address of the organization preparing the document. A DRDC Centre sponsoring a contractor's report, or tasking agency, is entered in Section 8.) C-CORE 4043 Carling Ave., Suite 202 Ottawa, Ontario Canada K2K 2A4 R-18-044-1443

2a. SECURITY MARKING (Overall security marking of the document including special supplemental markings if applicable.)

CAN UNCLASSIFIED

2b. CONTROLLED GOODS

NON-CONTROLLED GOODS DMC A

3. TITLE (The document title and sub-title as indicated on the title page.) Multi-Satellite Data Integration for Operational Ship Detection, Identification and Tracking (DIOS): Progress Report 1

4. AUTHORS (Last name, followed by initials – ranks, titles, etc., not to be used) Warren, S.; Zakharov, I.

5. DATE OF PUBLICATION (Month and year of publication of document.) January 2019

6a. NO. OF PAGES (Total pages, including Annexes, excluding DCD, covering and verso pages.)

38

6b. NO. OF REFS (Total references cited.)

71 7. DOCUMENT CATEGORY (e.g., Scientific Report, Contract Report, Scientific Letter.)

Contract Report

8. SPONSORING CENTRE (The name and address of the department project office or laboratory sponsoring the research and development.) DRDC – Valcartier Research Centre Defence Research and Development Canada 2459 route de la Bravoure Québec (Québec) G3J 1X5 Canada

9a. PROJECT OR GRANT NO. (If appropriate, the applicable research and development project or grant number under which the document was written. Please specify whether project or grant.)

05ec

9b. CONTRACT NO. (If appropriate, the applicable number under which the document was written.)

10a. DRDC PUBLICATION NUMBER (The official document number by which the document is identified by the originating activity. This number must be unique to this document.) DRDC-RDDC-2019-C064

10b. OTHER DOCUMENT NO(s). (Any other numbers which may be assigned this document either by the originator or by the sponsor.)

11a. FUTURE DISTRIBUTION WITHIN CANADA (Approval for further dissemination of the document. Security classification must also be considered.)

Public release

11b. FUTURE DISTRIBUTION OUTSIDE CANADA (Approval for further dissemination of the document. Security classification must also be considered.)

12. KEYWORDS, DESCRIPTORS or IDENTIFIERS (Use semi-colon as a delimiter.)

Maritime Domain Awareness; Arctic; Detection and Target Tracking; Target Detection, Tracking and Multi-Sensor Fusion; Ship and Ice discrimination

13. ABSTRACT/RÉSUMÉ (When available in the document, the French version of the abstract must be included here.)

This document comprises the Progress Review Report 1 as a deliverable under the Defence Industrial Research Program (DIRP) Contract No. W7714-186608/001/sv. It provides an update on the technical and management process for Multi-Satellite Data Integration for Operational Ship detection, identification and tracking (DIOS) since the kick-off meeting held on September 6, 2018.

Ce document est le Rapport de Progrès 1 en tant que livrable sous le Programme de recherche industrielle pour la défense (PRID) Contrat No. W7714-186608/001/sv. Il présente une mise à jour des progrès techniques pour le project Multi-Satellite Data Integration for Operational Ship detection, identification and tracking (DIOS) depuis la rencontre de début de projet du 6 septembre 2018.

multi-satellite data integration for operational ship

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