THREE-DIMENSIONAL IMAGING OF SUBMARGED OBJECTS BY SIDE-SCAN SONAR DATA PROCESSING Krzysztof Bikonis, Zbigniew Lubniewski, Marek Moszynski, Andrzej, Stepnowski Gdansk University of Technology POLAND

2 Outline  Applications of Side Scan Sonar in 3 D seafloor reconstruction  Shape from Shading (SFS) technique  Application of SFS to 3 D seafloor reconstruction  3 D Seafloor reconstruction  Results  Conclusions

3 Application of 3 D seafloor reconstruction (1) Elevation maps have obvious geological significance, and they can be used for autonomous underwater vehicle (AUV) navigation as an AUV must know the shape of the seafloor around it. (D. Langer, M. Hebert, Building Elevation Maps From Underwater Sonar Data, 1994) 2 D side scan sonar image3 D reconstruction from 2 D SSS image AUV on the bottom

4 Application of 3 D seafloor reconstruction (2) Elevation maps can be used for routes planning on the seafloor for oil and gas pipelines. 2 D side scan sonar image 3 D reconstruction from 2 D SSS image Plan of pipe route (D. Langer, M. Hebert, Building Elevation Maps From Underwater Sonar Data, 1994)

5 Application of 3 D seafloor reconstruction (3) Elevation maps can be used for planning of foundations on the seafloor for oil and gas platforms. 2 D side scan sonar image3 D reconstruction from 2 D SSS image Platform project (D. Langer, M. Hebert, Building Elevation Maps From Underwater Sonar Data, 1994)

6 Shape from shading (SFS) technique Classical approach in computer graphics (Horn, 1970)  Suppose reflected light depends only on α radiance = k*cosα

7 Shape from shading (SFS) technique The reflectance map ImageReflectance map Image

8 Shape from shading (SFS) technique The reflectance map Image Reflectance map: R N=[p q –1]

9 Shape from Shading (SFS) Three approaches:  Characteristic Strip Method (Horn, 77) - select a few points where normal is known - grow solution by moving direction of R  Variational Method (Ikeuchi & Horn, 81) - start with an initial guess of surface shape - define energy function - refine to minimize energy function  Photometric Stereo (Woodham, 80) - use more images Δ

10 Shape reconstruction  Minimization – minimization approaches obtain a solution by minimizing an energy function  Propagation – propagation approaches propagate the shape information from a set of surface points (e.g. Singular points) to the whole image  Local – local approaches derive shape based on the assumption of surface type  Linear – linear approaches compute the solution based on the linearization of the reflectance map (R. Zhang, P. S. Tsai, J. E. Cryer, M. Shah, Shape from shading: A survey, 1999)

11 Shape reconstruction (synthetic data) Synthetic images generated using two different light sources: (a) Synthetic Vase (0; 0; 1). (b) Mozart (0; 0; 1). (c) Synthetic Vase (1; 0; 1). (d) Mozart (1; 0; 1). (R. Zhang, P. S. Tsai, J. E. Cryer, M. Shah, Shape from shading: A survey, 1999)

12 Depth maps for the Synthetic Images: (a) Synthetic Vase. (b) Mozart. Results for Tsai and Shah's method on synthetic images: (a) Vase. (b) Mozart. (a1) and (b1) show the results for test images with light source (0; 0; 1). (a2) and (b2) show the results for test images with light source (1; 0; 1). Shape reconstruction (Linear)

13 Shape reconstruction (real data) Real images: (a) Lenna. (b) Pepper. (c) Vase. (R. Zhang, P. S. Tsai, J. E. Cryer, M. Shah, Shape from shading: A survey, 1999)

14 Results for Zheng and Chellappa's method on real images: (a) Lenna. (b) Pepper. (c) Vase. Results for Bichsel and Pentland's method on real images: (a) Lenna. (b) Pepper. (c) Vase. Results for Pentland's method on real images: (a) Lenna. (b) Pepper. (c) Vase. Results for Tsai and Shah's method on real images: (a) Lenna. (b) Pepper. (c) Vase. Shape reconstruction (real data)

15 Application of SFS to 3 D seafloor reconstruction  Amplitude processing  Calculating the height of an objects

16 Amplitude processing a)Acoustic wave output from sonar transducer b)Bottom contour c)Returned echo signal d)TVG processed echo signal e)Rectified TVG output (J. M. Cuscheri, M. Hebert, Three-Dimensional Map Generation From Side-Scan Sonar Images, 1990)

17 Calculating height of an objects Where H t is the height of the object, R s is the range to the object, L s is the length of the shadow, and H f is the altitude of sonar transducer above the bottom

18 Results for simulated data a)Actual contour with acoustic shadow zones b)Rectified sonar output c)Estimated contour

19 3 D seafloor reconstruction Prerequisites:  acoustic wave propagates in water column along approximate straight line,  reflectivity model is known,  altitude h of the tow fish is known,  the dimensions along vertical (z) axis of a bottom to be reconstructed are small in comparison with the tow fish altitude.

20 3 D seafloor reconstruction Geometry Surface backscattering coefficient  h - the sonar tow fish altitude  N - surface normal vector   - bottom slope angle   - incidence angle   - transmission angle  z - altitude  (1) Lambert’s Law-like  (2) linear

21 Visualization technique

22 Example of 3 D seafloor reconstruction 2 D side scan sonar image 3 D reconstruction from 2 D SSS image 3 D reconstruction from 2 D SSS image with bathymetry

23 USS Utah short history  Utah (Battleship No. 31) was Laid dawn on 9 March 1909 at Camden N.J.  When the war ended Utah was ordered to serve as honor escort for the transport George Washington that was carrying President Woodrow Wilson to the Versailles Peace Conference.  On April 30, 1935, Utah joined the Pacific Fleet for a cruise to the Hawaiian Islands.  7:55 A.M., Sunday, December 7, 1941, Japanese attacked Perl Harbor.  The wreck of Utah is relatively in a good condition, and to visit Utah you need a boat...

24 Example of 3 D wreck reconstruction This 600 kHz image of the USS Utah was imaged by the U.S. Army 7th Engineer Detachment (Diving) during initial water training on their new Sea Scan® Centurion TM system.

25 Example of 3 D wreck reconstruction

26 Example of 3 D wreck reconstruction

27 Conclusions  Data acquired by side-scan sonars requires amplitude processing for estimating depth to the bottom values  Side-scan sonar data tends to be very noisy  All the SFS algorithms produce generally poor results  Area classification required for proper reflection map determination  The results presented are preliminary as the authors used simple 1 D algorithm for 3 D seafloor reconstruction  Future: Construction of the algorithm that imitates the process of human eye analysis for 3 D shape reconstruction