This project has received funding from the European Union s Horizon 2020 Research and Innovation Programme under grant agreement no 687289 Co-ReSyF RA lecture: Vessel detection and oil spill detection Coastal Waters Research Synergy Framework Eimear Tuohy (UCC), Nuno Grosso (Deimos) (delivered by Eirini Politi, UCC)
Lecture outline 2 Aim of this lecture Introduction EO-based methods Satellite sensors & data Example applications Co-ReSyF processing chain Conclusion Contact us
Aim of this lecture 3 The aim of this lecture is to introduce oil spill detection and vessel detection methodologies using satellite data Describe how these applications are provided in Co-ReSyF www.esa.int www.esa.int
Introduction Why? 4 Satellite EO offers us the opportunity to detect, observe and monitor oil spills and vessel locations and movement, often in remote and inaccessible areas. Why is this an important use of EO sensors? Oil Spills Detect, monitor and aid in the modelling of the spread of oil slicks Provision of key information to environmental response teams Monitoring of leaks from undersea pipelines and offshore infrastructure Illegal emptying of billage tanks in open water Vessel Detection Monitoring of busy marine shipping lanes Fisheries management Search and rescue Detection vessels engaged in illegal activity
EO-based methods 5 Synthetic Aperture Radar (SAR) SAR is an active microwave sensor, which captures two dimensional images of the Earth s surface. To create a SAR image, successive pulses of radio waves are transmitted to "illuminate" a target scene, and the echo of each pulse is received and recorded. The brightness of the captured image depends on the properties of the target surface Detection of features relies on the interaction of the microwave energy and the surfaces it is being reflected off.
Polarisation 6 SAR uses an antenna that is designed to transmit and receive electromagnetic waves of a specific polarisation. A radar system can have the following channels: HH - for horizontal transmit and horizontal receive; VV - for vertical transmit and vertical receive HV - for horizontal transmit and vertical receive; VH - for vertical transmit and horizontal receive Co-Polarised Signal (HH, VV): Usually strong; Specular, surface or volume scattering Cross-Polarised Signal (HV, VH): Usually weak; Associated with multiple scattering; Strong relationship with orientation of targeted object(s) Examples of usage in our case: VV polarised SAR acquisitions are usually preferred for oil spill detection because they give higher radar backscattering from the sea surface, and therefore provide more contrast when oiis floating on the sea surface. HH and HV SAR acquisitions generally enhance the contrast between a vessel (bright object) and the surrounding sea surface (dark background), facilitating the ship detection.
EO-based methods 7 Positives SAR data may be collected day or night All weather capability Unaffected by cloud coverage Very high spatial resolution Possible Issues Look-alikes" may be detected - (e.g. natural films, low wind surfaces, internal waves) give similar backscatter values to oil spills Complex processing Speckle effects (due to diffuse reflection from rough objects, e.g. sea) Visual interpretation not as intuitive as for optical images
EO-based methods 8 1. Oil Spill Detection of oils spills relies on the fact the oil makes the water surface appear smoother, thus decreasing backscattering. The oil damps short surface waves and thus reduces the backscattered radar power over these areas. This appears as a dark area that is distinctly contrasting to the brightness of the radar backscatter produced by wind-generated ripples. www.esa.int
EO-based methods 9 2. Vessel Detection Ships appear as bright objects in SAR images because, in contrast to surrounding water, they are strong reflectors of the radar pulses emitted by the satellite. risp.nus.edu.sg Further details such as ship length, direction of travel and velocity may also be derived from SAR data. In images with finer resolution e.g. RADARSAT, it is possible to identify the structure of ships. Complimentary AIS data can be used to identify ships detected.
Satellite sensors commonly used for these applications: www.unavco.org
Example applications 11 Disaster Response ESA provide satellite data to rescue authorities and environmental agencies in times of need ENVISAT ASAR image of the Prestige Oil spill off Spain 17/11/2002 ENVISAT ASAR image of the Deepwater Horizon Oil Spill in the Gulf of Mexico 02/05/2010
Example applications 12 Ship Traffic Monitoring SAR imagery may be combined with an automatic ship identification system (AIS) to provide a powerful tool in vessel detection and identification. SAR can also infer a vessel s speed and direction of travel, if a wake is present. SAR-AIS COMPARISON??? False alarm? Illegal activity?? False alarm???
Co-ReSyF Processing Chain 13 As part of the Co-ReSyF platform, both oil spill detection and vessel detection modules will be available for your use. The objective of these modules is to provide a robust and easy to use processing chain. The user will simply have to identify: Area Of Interest (AOI) Date (or date range) for satellite data collection Preferred threshold values (can use default) All pre-processing and processor (algorithm) will then automatically run Output GeoTiff
Co-ReSyF Processing Chain - Oil Spill Detection 14 The CoReSyF oil spill module not only applies the detection methodology but also: Provides access to the raw data Applies the necessary pre-processing steps SAR Image Selection Pre Processing: Calibration, geometric correction, speckle filtration, land masking Oil Spill Detection GeoTiff output
Co-ReSyF Processing Chain - Vessel Detection 15 The Co-ReSyF vessel detection module is based on the SNAP Ocean Object Detection tool: Threshold constant false alarm (CFAR) detector Accurate results Easily integrated into a python processing chain VV and VH exhibit different results which one do I trust? Output from SNAP detection algorithm, Cobh, Ireland
Co-ReSyF Processing Chain - Vessel Detection 16 The module allows the user to identify their AOI and date range, apply the pre-processing techniques and apply the vessel detection algorithm in an intuitive manner SAR Image Selection Pre Processing: Ellipsoid correction, Subset, Land mask, Vessel Detection Algorithm GeoTiff Output and xml file Radiometric calibration
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Conclusion 18 SAR EO data can be accurate, timely, consistent and offer a large (spatial) scale. However, they can also be: Technically difficult to process Data may be negatively affected by environmental factors Poor temporal resolution A large amount of confusing sources for data and of processing methodologies
Conclusion 19 Co-ReSyF strives to address these issues and make EO data processing for oils spill and vessel detection accessible to all scientists regardless of their love or hate of EO data processing!! Easy-to-use interface Simple and repeatable methodology No need for algorithm development No need for coding expertise
Conclusion 20 Traditional download methods: Each SAR image is approx. 1.2Gb Data storage issues Data access issues Confusing preprocessing and algorithm application CoReSyF Modules: No need to download raw data No storage issues No access issues Intuitive interface Community help and advice
Going Forward 21 A more efficient and streamlined processing chain Add speckle filtration Improve land masking/buffering for areas with land present Improve metadata output: vessel length, direction.
Thank you for listening! 22 Eimear Tuohy UCC eimear.tuohy@ucc.ie Nuno Grosso Deimos nuno.grosso@deimos.com.pt