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CSIRO OCEANS AND ATMOSPHERE
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel-1 SAR satellite
David Blondeau-Patissier, Thomas Schroeder, Foivos Diakogiannis, Paul Irving, Christian Witte, Andy Steven
Pacific Islands GIS & RS User Conference 2019, 25th - 29th November 2019, Suva
In the context of Fiji
2 | Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Regional context
Viti Levu seen from Sentinel-1 SAR
3 |
Sentinel-1A SAR scene
Acquired: 24th November 2019
Overpass time: 05.40am local time
Frequency: every 6 days (ascending and descending pass)
Viti Levu
Note: Satellite imagery from the Sentinel satellites is free
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Regional context
Content
4 |
1. Synoptic overview of the research Objectives
a) Milestones
b) Key outcomes
2. Methodology and Example cases
3. Project outcomes and future work
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Outline
• ContextNo regular routine, broad scale monitoring of oil spills
• ObjectiveTo develop a semi-, or fully-automated satellite-imagery-based oil spill detection system.
5 |
This project - Context and objective
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Context & Objectives
6 |
Synthetic Aperture Radar
Ocean colour,SST
Multispectral
The European Space Agency’s Sentinels
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | The Sentinels
7 |
Our main challenges – true vs false positives
Biogenic slicks
Oil slicks
Wind shadows
Lucinda jetty coastal observatory
Townsville
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Key challenges
Key challenges
• Size of the Sentinel-1 SAR scenes
• Complexity of differentiating true-positives (oil spill features) from other features
• Time difference between Sentinel-1 SAR and the other Sentinels, Sentinel-2 / -3
• Processing of the SAR images
• We thought there would be a better coverage from Sentinel-1 SAR i.e., less than 12 day repeat
• Added complexity of the Reef matrix when interpreting the images
… and many more
8 | Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Key challenges
Methodology and
Example cases
9 | Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Methodology and Example cases
10 |
Sentinel-1 SAR coverage – Great Barrier Reef
Sentinel-1ASentinel-1B EEZ
Oct 2014 – Nov 2019
Ntotal: >3,000 scenes
Average: 75/month
Great Barrier Reef Fiji
348,700 km2 18,275 km2
75 scenes/month 10 scenes/month
Comparison Great Barrier Reef - Fiji
Month of October
Day of the month
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Num
ber
of
Sentinel-
1 S
AR
scenes
0
1
2
3
10 scenes/month
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Acquisitions
Presentation title | Presenter name11 | https://emergency.copernicus.eu
The service can be provided in:• 'rush' mode: for emergency management
activities that require immediate response• 24/7 basis• products are provided ASAP (from a few
hours to a few days after the user request)
• 'non-rush' mode: to support activities that do not require immediate response, i.e. for prevention, preparedness, disaster risk reduction and recovery phases.
12 |
Our database
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Database
13 |
Our database
MauritiusOil Spill captured by Sentinel-1 on 05 February 2019
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Database
Dark area
detection
Features
extraction
Classification
1st Step
2nd Step
3rd Step
SAR image
Results
14 |
Sentinel-1 SARImage processing
Sentinel-1 SARImage processing
Rule-based approach ( selected
parameters)
Use of convolutional neural networks (image based)
Semi-empirical approach Parametric approach
METHOD 1 METHOD 2
Methodology – Rule based vs Machine learning
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Methodology
15 |
METHOD 1 Rule-based approach
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Rule-based approach
16 |
METHOD 2 Machine Learning (CNN)
• Use of Python and PyTorch
• Computer vision-based approach
• Convolutional neural network architecture
• Dataset of 3,296 labelled “chips” used as input for the training phase
• Work in progress but promising results
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Convolutional neural networks
Cape York, Northern Australia
ESA; Sentinel-1 SAR scene of07th November 2018(085337UTC)
Reefs
LAND MASS
ship
oil
Inset from left
North West Shelf
ESA; Sentinel-1 SAR scene of09th July 2019(213920UTC)
LAND MASS
Inset from left
Wandoo-B platform(Vermilion Oil and Gas)
oil
oil
Karratha
North West Shelf
ESA; Sentinel-1 SAR scene of09th April 2018(213909UTC)
LAND MASS
oil
Inset from left
oil
Karratha
Wandoo-B platform(Vermilion Oil and Gas)
Outcomes and future workDatabase of past and current events
Processing workflow for Sentinel-1 SAR
Continuously updated archive and on-demand processing
Automated approaches for oil spills detection
Processing workflow can be relocated to .e.g. South Pacific / Fiji
20 |
• Synergy between various satellite sources, models for improved detection
Satellite detection of marine pollution in Australian coastal waters using the Copernicus Sentinel satellites | Future work
David Blondeau-PatissierResearch ScientistCSIRO Oceans and Atmosphere
e David.Blondeau-Patissier@csiro.au
Thank you
OCEANS AND ATMOSPHERE
Thomas SchroederSenior Research ScientistCSIRO Oceans and Atmosphere
e Thomas.Schroeder@csiro.au
Suva
Aggregation of Sentinel-1 images acquired between January 2018 and November 2019.
Mapping major sea lanes from the Google Earth Engine
Synthetic Aperture Radar workshop – THIS FRIDAY22 |
This Friday
Please contact Amy Parker
Zheng-Shu Zhou
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