Extraction of Water Surfaces in Ka-band SAR Images of KaRIN on SWOT

1. 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 ParisTech 2 Centre National d’Etudes Spatiales (CNES) Thursday, 28/07/11, Vancouver, Canada, IGARSS 2011

2. 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)

3. Outline Index • Context • Principle of the network extraction method • Adaptation to hydrological network of SWOT data • Results and evaluation • Conclusion

4. SWOT SAR data SWOT SAR data • Simulated SWOT data • North Camargue test site • 4 incidence angles • Different cases Spot image SWOT SAR image [pt4,c1]

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

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

7. Outline Index • Context • Principle of the network extraction method • Adaptation to hydrological network of SWOT data • Results and evaluation • Conclusion

8. Network extraction scheme 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

9. Low-level step 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: Merge D1 & D2

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

11. High-level step Basic theory • Graph construction 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. page 11

12. High-level step 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): • Labeling Graph : 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 page 12

13. Limits Overview • 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 page 13

14. Outline Index • Context • Principle of the network extraction method • Adaptation to hydrological network of SWOT data • Results and evaluation • Conclusion

15. Adaptation to hydrological network of SWOT Overview • 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 page 15

16. Improvements at low-level step Adaptations 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. amplitude • For SWOT image: add the amplitude information to suppress the occurrence of roads in river extraction. page 17

18. Without amplitude information With amplitude information • For SWOT image: add the amplitude information to reduce the false alarm (variation along swath) page 18

19. 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. amplitude original improved page 19

20. Improvements at high-level step Adaptations 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. Graph building Adaptations at high-level step • Problems • Too many useless connections River 1 River 2

22. Graph building Adaptations at high-level step • Solutions • Reduce useless connections • 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 Component 1 Component 2

23. Graph building Adaptations at high-level step Local repositionning of extremities to improve the added segments Add an extra connection • 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.

24. Graph building Adaptations at high-level step • 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

25. Outline Index • Context • Principle of the network extraction method • Adaptation to hydrological network of SWOT data • Results and evaluation • Conclusion

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

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.

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. Conclusion Overview • 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) page 29

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. Thank you! Questions?