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

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

Abstract

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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