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Near real-time flood detection in urban and rural areas using TerraSAR-X
David Mason, Ian Davenport,
University of Reading
Guy Schumann, Jeff Neal, Paul Bates
University of Bristol
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Need for real-time visualisation tools
• Pitt Commission set up by UK government recommended real-time visualisation tools be available for emergency reponders
• Vast majority of flooded area may be rural, but important to detect urban flooding due to increased risks/costs
• ASAR/ERS-2 have too low a resolution to detect urban floods – but high resolution SARs can.
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Fusion of algorithms
• Near real-time algorithm for rural flood detection developed at DLR
• Non-real-time algorithm for urban flood detection developed at Reading
• Objective is to fuse and automate to develop near real-time algorithm for flood detection in urban and rural areas
• Algorithm assumes LiDAR available for urban area
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2km
A
B
N
A
B
TerraSAR-X image of the Severn flood of July 2007
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TerraSAR-X image of Tewkesbury flooding on 25th July 2007 showing
urban areas (3m resolution, dark areas are water).
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ASAR image of 26th July 2007 (25m resolution).
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Aerial photo mosaic of Tewkesbury flooding on 24 July 2007.
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LiDAR DSM of Tewkesbury (2m resolution).
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Layover (AB) and shadow (CD) in a flooded street between
adjacent buildings.
h1h2
A N B Y C D
O
θ
TerraSAR-X
M
R
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Regions unseen by TerraSAR-X in LiDAR DSM due to combined
shadow and layover (satellite looking West).
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Detection of rural flooding
• Detect flood extent in rural areas, then in urban area guided by rural flood extent
• Rural flood detection achieved by segmenting SAR image into homogeneous regions (objects), then classifying them
• Use eCognition Developer software for multi-resolution segmentation and classification.
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Threshold determination
0
10
20
30
40
50
60
70
80
40 50 60 70 80
Object mean intensity threshold T
Per
cen
tag
e
Misclassified water (%)
Misclassified non-water (%)
Total misclassified (%)
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Flood detection in urban areas
• Seed pixels identified with backscatter less than threshold, and heights less than or similar to adjacent rural flood
• Seed pixels clustered together if sufficiently close
• Shadow/layover masked out
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Correspondence between the TerraSAR-X and aerial photo flood extents in main urban areas of Tewkesbury, superimposed on the LiDAR image (yellow = wet in SAR and aerial photos, red = wet in SAR only, green = wet in aerial
photos only). Flood detection accuracy = 75%.
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Correspondence between the TerraSAR-X and aerial photo flood extents over the rural validation area (region B), superimposed on the TerraSAR-X image (blue = wet in SAR and aerial photos, red = wet in SAR only, green = wet in
aerial photos only). Flood detection accuracy = 89%.
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(a)
(b)
Possible multi-scale visualisation of flood extents in (a) rural (blue = predicted flood), and (b) urban areas (yellow = predicted flood).
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Operational considerations
• Ensure that –– can task a satellite in time to acquire image of developing flood– short time delay between image acquisition and production of
SAR flood extent
• Preprocessing operations can be carried out in parallel with tasking satellite e.g. generation of shadow-layover map
• Blueprint for operational system is ESA FAIRE system – produces multi-look geo-registered ASAR images 3 hours after acquisition
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Conclusion
• Automatic near real-time algorithm developed that can detect rural flooding with good accuracy, urban flooding with less good accuracy
• Need to test on more flood events
• Need to improve urban classification accuracy