Assessment of Sea Surface Signatures for Naval Platforms Using SAR Imagery (AssenSAR)

Lead Research Organisation: University of Bristol
Department Name: Electrical and Electronic Engineering


In space imaging, enhanced image quality is key to the detection and characterisation of difficult and transient targets. For example, accurate evaluation of the sea surface conditions can help with the detection and characterisation of ship wakes. These provide key information for tracking (illegal) vessels and are also useful in classifying the characteristics of the wake generating vessel.

Until recently, one of the main factors hampering research into sea surface modelling was the lack of sufficient data of high enough quality, able to accurately describe the sea surface. Remote-sensing technologies have however shown remarkable progress in recent years and the availability of remotely sensed data of the Earth and sea surface is continuously growing. Several European missions (e.g., the Italian COSMO/SkyMed or the German TerraSAR-X) have developed a new generation of satellites exploiting synthetic aperture radar (SAR) to provide spatial resolutions previously unavailable from space-borne remote sensing. The UK is currently developing the first of a constellation of four satellites that will constitute the NovaSAR mission. This represents a milestone for Earth-observation capabilities but also requires the development of novel image modelling, analysis, and processing techniques, able to cope with this new generation of data and to optimally exploit them for information-extraction purposes.

Indeed, the mathematical modelling and understanding of wakes and other sea surface signatures can be greatly enhanced through image analysis and information extraction from SAR imagery. Hence, this project is concerned not only with the development and validation of new sea surface models, but also with the design of very advanced methods for enhancing SAR image quality and for subsequent information extraction.

The results of this project will be important in the detection and tracking of illegal vessels involved in smuggling goods or humans. They will also be indicative in terms of understanding and classifying the characteristics of the wake generating vessel. As a consequence, the work will directly benefit the design of stealthy vessels that can avoid such detections, reducing the risk to naval operations.

Planned Impact

AssenSAR is highly relevant to space imaging and hence to the UK's satellite industry (one of the Eight Great Technologies) where enhanced image quality can be exploited to detect and characterise difficult and transient targets.

The results of this project will be important in the detection and tracking of illegal vessels involved in smuggling goods or humans. They will also be indicative in terms of understanding and classifying the characteristics of the wake generating vessel. The work will thus inform the design of stealthy vessels that can avoid such detections, reducing the risk to naval operations.

The range of commercial and societal impact objectives are as follows:

Capability: We will help to shape UK capability across the satellite industry through better understanding of remote sensing imagery and its utility in information extraction in marine environments. Our researchers will be trained with the technical and enterprise skills needed to deliver the impact of this research across the wider community.

Commercial: Close coupling with partner organisations will facilitate pull-through into advanced products, opening new opportunities for innovation in both marine surveillance and remote sensing imaging. By liaising with the UK Satellite Applications Catapult, it is expected that the innovation potential of this project will be nurtured and eventual commercialisation of research will be facilitated. There are significant foreseeable opportunities for the creation of new starts-up to exploit the outcomes of this project.

Operational Practice: We will concentrate on the design of new denoising and super-resolution protocols for situations where automated processing is used to support human decision making. Obvious examples are in the viewing and analysis of remote sensing imagery and information extraction therefrom.


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Karakus O (2020) Ship Wake Detection in SAR Images via Sparse Regularization in IEEE Transactions on Geoscience and Remote Sensing

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Pappas O (2018) Superpixel-Level CFAR Detectors for Ship Detection in SAR Imagery in IEEE Geoscience and Remote Sensing Letters

Description 1) In order to detect ship wakes, one important first step is to detect the wake-generating vessel. That provides the starting point from which the search for sea wakes can begin. In this context, the AssenSAR team have developed a novel, superpixel-based topology for the detection of ships at sea in synthetic aperture radar (SAR) remote sensing imagery. The method is based on the so-called Constant False Alarm Rate (CFAR), which are detectors that utilise various statistical models for sea clutter in conjunction with thresholding schemes. Superpixels on the other hand are groups of connected pixels with similar colors or gray levels. The architecture based on the combination of superpixels and CFAR can be utilized with most prior sea clutter statistical models or thresholding schemes. The performance of this modified SP-CFAR algorithm was demonstrated on TerraSAR-X and SENTINEL-1 images, with superior results in comparison to classical CFAR for various background distributions.
2) Following accurate detection of ship targets in SAR images, the team have also developed a method for the detection of corresponding ship wakes. Since ship wakes mostly appear as lines in SAR images, we have used line detection methods for their identification. Unlike classical approaches that use direct line detection methods, for example based on the Radon transform, our proposed method uses an inverse problem formulation that relies on very advanced mathematical concepts related to non-convex optimisation. We have conducted extensive experiments involving a high number of ship wakes visible in SAR images acquired using different satellite platforms. The performance of our proposed method was demonstrated to be superior to that of previously proposed techniques with a significantly higher detection performance.
3) The visibility of sea wakes in SAR images is enabled by the interaction of the electromagnetic waves with the water surface. This is called Bragg resonance scattering and relates to both characteristics of the sea state (amplitude of waves and their direction) and SAR signal (wavelength, incidence angle of signal, etc). In order to help understanding all these phenomena and be able to assess the detectability of ship wakes we have also developed a SAR image simulator for the ocean surface. We have not yet published our findings in this respect, but a conference paper has been submitted and a journal paper is in preparation. We anticipate that this will enable significant advances in ship and ship-wake detection performance. Our plan is to develop machine learning approaches for this purpose, which will benefit from the data augmentation capability generated through our developed simulator.
Exploitation Route Applications of our ship and ship wake detection algorithms can be related to a diverse set of problems such as navigation and equipment positioning; radar from manned or unmanned platforms and satellites for object detection, classification, tracking, or avoidance; remote sensing of oceanographic phenomena such as fronts and eddies; law enforcement and monitoring for possible drug trafficking and illegal immigration.
One interesting potential application that emerged following our interactions with the Satellite Applications Catapult (SAC) is in illegal fishery identification. In particular, we have started talking to OceanMind, a spin-out from SAC, who provide insights and intelligence into fishing compliance to government authorities and seafood buyers, in order to enable enforcement and compliance to protect the world's fisheries. Our methods could form the basis of machine learning algorithms that would classify detected ships not associated to automatic identification systems and could hence potentially perform illegal fishing activities.
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Environment,Government, Democracy and Justice,Security and Diplomacy,Transport