1. 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 Cao 1, Florence Tupin 1, Jean-Marie Nicolas 1, Roger Fjørtoft 2, Nadine Pourthié 2 1 Institut Télécom, Télécom ParisTech 2 Centre National d’Etudes Spatiales (CNES)

2. 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

3. Telecom ParisTechpage 3 Context Principle of the network extraction method Adaptation to hydrological network of SWOT data Results and evaluation Conclusion IndexOutline

4. Telecom ParisTechpage 4 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

5. Telecom ParisTechpage 5 Incidence angle 1 ° 2 ° 3° 4° [pt1][pt2] [pt3][pt4] From pt4 to pt1, resolution decreases. More difficult to extract river!

6. Telecom ParisTechpage 6 The masks of water 1. Overview amplitude water mask [pt1] [pt1,c1] - Valuable reference - 4 incidence angles (pt1-4) Ground truth

7. Telecom ParisTechpage 7 Context Principle of the network extraction method Adaptation to hydrological network of SWOT data Results and evaluation Conclusion IndexOutline

8. Telecom ParisTechpage 8 Basic theory Line extraction approach: Road detection method for satellite image 2 main steps Low-level step High-level step In between Hough transform, thinning & linearization Linear network extraction scheme Network extraction scheme

9. Telecom ParisTechpage 9 Basic theory For each pixel and each direction Define a mask of regions Calculate the line detector resp. Ratio edge detector D1: Cross-correlation line detector D2: Low-level step Merge D1 & D2

10. Telecom ParisTechpage 10 Results of low-level step Thinning Linearization (obtained segments) 1 connex component contains at least 1 segment

11. Telecom ParisTechpage 11 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

12. Telecom ParisTechpage 12 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 network c represents a clique of the graph s node of the graph is a segment d the observation l label 0 or 1 High-level step Labeling Graph

13. Telecom ParisTechpage 13 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

14. Telecom ParisTechpage 14 Context Principle of the network extraction method Adaptation to hydrological network of SWOT data Results and evaluation Conclusion IndexOutline

15. Telecom ParisTechpage 15 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

16. Telecom ParisTechpage 16 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

17. Telecom ParisTechpage 17 For SWOT image: add the amplitude information to suppress the occurrence of roads in river extraction. amplitude

18. Telecom ParisTechpage 18 Without amplitude informationWith amplitude information For SWOT image: add the amplitude information to reduce the false alarm (variation along swath)

19. Telecom ParisTechpage 19 originalamplitudeimproved 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.

20. Telecom ParisTechpage 20 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

21. Telecom ParisTechpage 21 Problems Too many useless connections Adaptations at high-level stepGraph building River 1 River 2

22. Telecom ParisTechpage 22 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

23. Telecom ParisTechpage 23 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

24. Telecom ParisTechpage 24 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

25. Telecom ParisTechpage 25 Context Principle of the network extraction method Adaptation to hydrological network of SWOT data Results and evaluation Conclusion IndexOutline

26. Telecom ParisTechpage 26 Most of the rivers are detected in the image, except a few very thin rivers The results Ground truth Extracted rivers Ground truth Extracted rivers

27. Telecom ParisTechpage 27 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

28. Telecom ParisTechpage 28 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

29. Telecom ParisTechpage 29 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)

30. Telecom ParisTechpage 30 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

31. Telecom ParisTechpage 31 Thank you! Questions?