CaoTupinThursday20110722.ppt

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

Telecom ParisTechpage 3

ContextPrinciple of the network extraction methodAdaptation to hydrological network of SWOT data Results and evaluationConclusion

IndexOutline


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


Incidence angle 1 ° 2 ° 3° 4°[pt1] [pt2] [pt3] [pt4]

From pt4 to pt1, resolution decreases. More difficult to extract river!


The masks of water

1. Overview

amplitude water mask [pt1][pt1,c1]

- Valuable reference

- 4 incidence angles (pt1-4)

Ground truth



IndexOutline


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


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


Results of low-level step

Thinning

Linearization (obtained segments)

1 connex component contains at least 1 segment


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


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):

page 12

: 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


Overview

page 13

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



IndexOutline


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


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

Telecom ParisTechpage 17page 17

For SWOT image: add the amplitude information to suppress the occurrence of roads in river extraction.

amplitude


Without amplitude information With amplitude information

For SWOT image: add the amplitude information to reduce the false alarm (variation along swath)


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.


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


Problems• Too many useless connections

Adaptations at high-level stepGraph building

River 1

River 2


Solutions• Reduce useless connections


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


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


The results show that using the new criteria, we have much less connections


Original Improved

The optimization step (simulated annealing)is easier on a smaller graph



IndexOutline


Most of the rivers are detected in the image, except a few very thin rivers

The results

Ground truth Extracted riversGround truth Extracted rivers


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.

page 27


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


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)


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


Thank you!

Questions?

CaoTupinThursday20110722.ppt

Technology

Transcript of CaoTupinThursday20110722.ppt