1. Using European Sentinel-2A to update
the Copernicus High-Resolution Layer
Imperviousness
Antoine Lefebvre1
, Christophe Sannier2
(1) CNES, UMR IRISA, Rennes, France
(2) SIRS SAS, Villeneuve d’Ascq, France
2. CONTEXT – Copernicus High Resolution Layers
• 5 Layers produced for 2012:
– Forest
– Grassland
– Wetland
– Permanent water bodies
– Imperviousness
• HRL Imperviousness, main specifications
– Resolution : 20x20m ; 100x100m
– Soil sealing degree : 0 – 100%
• Seamless European-wide coverage over 39
EEA countries
3. CONTEXT – Sentinel-2
• Launched on 23 June 2015
• 13 multi-spectral bands
– High Spatial Resolution :
• up to 10m
• Spectral capabilities :
– 3 bands in the red-edge
– 2 bands in the SWIR
• Repetitiveness :
– up to 5 days (with constellation)
4. • None provides a cloud-free coverage
• Incomplete time series (gaps)
– How to get an homogeneous result over Europe ?
– How to do it in an automatic and operational way ?
t1 t2
t3 t4
KEY IDEA – Exploiting Sezntinel-2
Repetitiveness
5. • Sum of overlays provides a cloud-free coverage
t1 t2
t3 t4 Sum of overlays
KEY IDEA – Exploiting Sezntinel-2
Repetitiveness
6. • Sum of overlays provides a cloud-free coverage
• Combination of single scene classification with data fusion technique
t1 t2
t3 t4 Sum of overlays
KEY IDEA – Exploiting Sezntinel-2
Repetitiveness
7. • 2 Former Urban Areas
– Prague, Czech Republic
• 6,900 km2
– Rennes, France
• 2,500 km2
• Sentinel-2 and Landsat-8
images
METHOD – Study Areas
8. • Input dataset
– All spectral bands
– Pantex
• Based on blue band (Sentinel-2)
• Based on Panchromatic band
(Landsat-8)
• Cloud mask extraction
– Sentinel-2: semi-automatic
– Landsat-8: F-mask
S2 PANTEX
S2 Cloud mask
S2 image
S2 image
METHOD – Data Preparation
9. • Sampling
– Automatic sampling on Copernicus High Resolution Layer (2012)
• 20x20m
• Range from 1 to 100%
• Available on all 39 EEA countries
• Classifier
– Random Forest
• Uncertainty
– Kappa
METHOD – Single Scene Classification
10. METHOD – Data Fusion
• Dempster-Shafer Theory (DST):
– Dealing with imprecision and uncertainty
• Kappa computed on each individual scene
• Associative rule:
– Combination of numerous separate information
• Sentinel-2, Landsat-8, … and some more if available
17. RESULTS - Benefits of multi-temporal data
Limitation of
commision errors
Urban areas
Agricultural areas
18. RESULTS - Benefits of multi-source data
Geometric
accuracy
enhancement
Urban areas
Imprecise edges
19. • Comparison: 2015 classifications and 2012 HRL Imperviousness
• Validation strategy (Built-Up / Non Built-Up)
– 400 randomly selected pixels and stratified selection
• 200 points in the change areas
• 200 points in the unchanged areas
– To prevent problems due to geometrical accuracy
• selected sample is surrounded by the same thematic class in a 3x3 window.
– Interpretation of samples is performed manually
Update Copernicus HRL Imperviousness
24. CONCLUSION
• Simple method to process Sentinel-2 time-series
• Overcome missing data
• Provide cloud-free coverage
• Sentinel-2 information
• Spectral resolution: cloud extraction, classification
• Spatial resolution: texture features
• Temporal resolution: repetitiveness
• Efficiency & Operationality
• Limited interaction with the user
• Automatic sampling (can be applied on the all 39 EEA countries)
• No image selection
• Good accuracy
• More overlays, less uncertainty, better accuracy
• Multi-source ability
• Complementarity with other sensors (Landsat-8 here but also, possible with any others)
