Coastal Waters Research Synergy Framework

Co-ReSyF RA lecture: Vessel detection and oil spill detection

Eimear Tuohy (UCC), Nuno Grosso (Deimos) (delivered by Eirini Politi, UCC)

This project has received

funding from the European

Union’s Horizon 2020 Research

and Innovation Programme under

grant agreement no 687289

Lecture outline

Aim of this lecture

Introduction

EO-based methods

Satellite sensors & data

Example applications

Co-ReSyF processing chain

Conclusion

Contact us

2

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

3Aim of this lecture

www.esa.int

www.esa.int

4Introduction – Why?

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

Synthetic Aperture Radar (SAR)

5EO-based methods

➢ 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.

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.

6Polarisation

7EO-based methods

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

1. Oil Spill

8EO-based methods

➢ 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

2. Vessel Detection

9EO-based methods

risp.nus.edu.sg

➢ 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.

➢ 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.

www.unavco.org

Satellite sensors commonly used for these applications:

Disaster Response

➢ ESA provide satellite data to rescue authorities and environmental

agencies in times of need

11Example applications

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

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.

12Example applications

SAR-AIS

COMPARISON

?

False alarm?

?

False alarm?

?

?

?

?Illegal

activity?

➢ SAR can also

infer a vessel’s

speed and

direction of

travel, if a

wake is

present.

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

13Co-ReSyF Processing Chain

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

- Oil Spill 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,

Radiometric calibration

Vessel Detection

Algorithm

GeoTiff Output and

xml file

Co-ReSyF Processing Chain

- Vessel Detection

17

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

18Conclusion

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

19Conclusion

Traditional download

methods:

➢ Each SAR image is

approx. 1.2Gb

➢ Data storage issues

➢ Data access issues

➢ Confusing pre-

processing and

algorithm application

20Conclusion

CoReSyF Modules:

➢ No need to download

raw data

➢ No storage issues

➢ No access issues

➢ Intuitive interface

➢ Community help and

advice

21Going Forward

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.

Nuno Grosso

Deimos

nuno.grosso@deimos.com.pt

22

Eimear Tuohy

UCC

eimear.tuohy@ucc.ie

Thank you for listening!