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Telecom ParisTech
Thursday, 28/07/11, Vancouver, Canada, IGARSS 2011
Extraction of water surfaces in simulated Ka-band SAR images of KaRIN on SWOT
Fang Cao1, Florence Tupin1, Jean-Marie Nicolas1, Roger Fjørtoft2, Nadine Pourthié2
1 Institut Télécom, Télécom ParisTech2 Centre National d’Etudes Spatiales (CNES)
Telecom ParisTech
Objective:• Detection of water surfaces for the high resolution mode of SWOT
KaRIN: development and testing of an extraction method for hydrological networks in Ka-band radar imagery at low incidence.
Device: KaRIN instrument of SWOT • Synthetic aperture radar with very low incidence angles (1° to 4°) • Ka-band (wavelength 8 mm) • Very small interferometric baseline (10m)
Specificities • Geometric distortions: lay-over, significant variation in the
resolution along the swath, specular reflections for flat surfaces• Large surface roughness (compared to images acquired by C or X-
band satellite systems)
page 2
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ContextPrinciple of the network extraction methodAdaptation to hydrological network of SWOT data Results and evaluationConclusion
IndexOutline
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SWOT SAR data
Spot image
SWOT SAR image [pt4,c1]
SWOT SAR data
Simulated SWOT data• North Camargue test site• 4 incidence angles• Different cases
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Incidence angle 1 ° 2 ° 3° 4°[pt1] [pt2] [pt3] [pt4]
From pt4 to pt1, resolution decreases. More difficult to extract river!
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The masks of water
1. Overview
amplitude water mask [pt1][pt1,c1]
- Valuable reference
- 4 incidence angles (pt1-4)
Ground truth
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ContextPrinciple of the network extraction methodAdaptation to hydrological network of SWOT data Results and evaluationConclusion
IndexOutline
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Basic theory
Line extraction approach:
Road detection method for satellite image
2 main stepsLow-level stepHigh-level step
In between
Hough transform, thinning & linearization
Linear network extraction scheme
Network extraction scheme
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Basic theory
For each pixel and each directionDefine a mask of regionsCalculate the line detector resp.
Ratio edge detector D1:
Cross-correlation line detector D2:
Low-level step
Merge D1 & D2
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Results of low-level step
Thinning
Linearization (obtained segments)
1 connex component contains at least 1 segment
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Basic theory
page 11
The segments detected in the low-level step are the input of the graph construction. Under certain conditions (angles, distance etc.), connections between segments are added to build the graph.
High-level step
Graph construction
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Basic theory
Labeling the segments with 2 labels: label 1 for “network” and label 0 for “not network”
The markovian labeling corresponds to an energy minimization (optimization with simulated annealing):
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: the likelihood term, which takes into account the radiometric properties of the data: the regularization term, which is linked to the shape of the networkc represents a clique of the graphs node of the graph is a segment d the observationl label 0 or 1
High-level step
Labeling Graph
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Overview
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Limits
SWOT images- River – bright lines.
- Width varies drastically.
Low-level step- Confusion roads / rivers
- Non detection of very thin rivers
High-level step• Graph construction
- Some very curved connections are missing
- Some false connections
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ContextPrinciple of the network extraction methodAdaptation to hydrological network of SWOT data Results and evaluationConclusion
IndexOutline
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Overview
page 15
Adaptation to hydrological network of SWOT
Adapt the whole algorithm to bright line detection Multi-scale analysis
Use multi-look to reduce the size of image and extract rivers at different scales.
Low-level step• Improvements of the line extraction algorithm
High-level step• Improvements of the algorithm in graph construction
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Adaptations at low-level stepImprovements at low-level step
Reduction of the road / river confusion :• New measure based on radiometry and merged with
D1 and D2 to reduce the confusion with roads
Improvement of the detection of very thin lines• Increase of the number of tested directions • New sizes of mask regions to detect very thin lines
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For SWOT image: add the amplitude information to suppress the occurrence of roads in river extraction.
amplitude
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Without amplitude information With amplitude information
For SWOT image: add the amplitude information to reduce the false alarm (variation along swath)
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originalamplitude improved
The sizes of detection regions are redefined to 7 cases to detect very thin lines (the width equals to 1–2 pixels) in images.
For SWOT image: increase the number of directions from 8 to16.
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Adaptations at high-level stepImprovements at high-level step
Graph construction• Original method:
- make as many as possible connections to be sure to have the solution in the graph
• Proposed method: build a smaller graph but with refined positioning of extremities- Better take into account high curvature river
- Simplification of the optimization step
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Problems• Too many useless connections
Adaptations at high-level stepGraph building
River 1
River 2
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Solutions• Reduce useless connections
Adaptations at high-level stepGraph building
Component 1
Component 2
Use the definition of connex component
2 kinds of extremities- Isolated extremities- Connected extremities
Do not make the connection if the extremities are connected extremities
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Local repositionning of extremities to improve the added segments
Add an extra connection
Adaptations at high-level step
• In SWOT image, there are some man-made drainages which are long and straight segments, and in between, they have small included angle (< 90deg)
• We make the connection if the connected segment is an extension of the detected segments.
Graph building
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The results show that using the new criteria, we have much less connections
Adaptations at high-level stepGraph building
Original Improved
The optimization step (simulated annealing)is easier on a smaller graph
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ContextPrinciple of the network extraction methodAdaptation to hydrological network of SWOT data Results and evaluationConclusion
IndexOutline
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Most of the rivers are detected in the image, except a few very thin rivers
The results
Ground truth Extracted riversGround truth Extracted rivers
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Quantitative analysis
Results evaluation
• TP: true positives are correct extracted pixels of rivers. • FP: false positives are misdetections • FN: false negatives are pixels which could not be
extracted by the line detection.
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Quantitative analysis
Generally we have high values of correctness and completeness (>0.5)
With different incidence angle (same case), the correctness and completeness are similar.
Case 2 has the best performance (>0.7)
Case 3 usually has lowest correctness and completeness (0.5-0.6)
Values are under-estimated due to bad relocalization of the network / ground truth
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Overview
page 29
Conclusion Contributions • Adaptation of a road network algorithm to the case of
hydrological network on SWOT data • Improvements :
- Adding of a new measure for road discrimination and improved line detector
- Building of a simplified graph (simplification of the optimization step, high curvature river)
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Future work
About the data• Test real SWOT SAR images• Verify the interferometric SAR images• Use the time-series SAR images• Use a prior information of the river position
Combination with other segmentation techniques to extract the whole water surfaces• Segmentations (snakes, region growing, etc.) for the
extraction of larger water surfaces such as lakes and wetlands
Use of connex component without linearization
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Thank you!
Questions?
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