Qualitative estimation of modeled Snell’s window stereo imagery for wind wave profile retrieval
https://doi.org/10.59887/fpg/p112-6ptf-fh5r
Abstract
On a qualitative level, the possibilities of using optical stereo images of the sea surface, registered from under the water, are considered in relation to the problem of remote diagnostics of waves. The task is implemented in a numerical experiment using a stereo image model of the Snell’s window (underwater image of the sky) for a given relief of the sea surface. The influence of the camera parameters, observation geometry, illumination and excitement on the quality of constructing disparity maps, necessary to restore the distances to the sighted elements of the sea surface, is analyzed. Recommendations on the methodology of a full-scale experiment in order to test the proposed method are formulated.
Keywords
remote sensing,
underwater imagery,
stereo imagery,
disparity,
Snell’s window,
sea roughness,
wind waves
Supporting agencies: The reported study was funded by RFBR, project number 19-35-60034
About the Author
A. A. Molkov
A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences; Institute of Applied Physics, Russian Academy of Sciences
Russian Federation
Russian Federation
119017, Pyzhevsky Per., 3, Moscow; 603950, Ulyanov Str., 46, Nizhny Novgorod
References
1. Cox C., Munk W. Measurements of the Roughness of the Sea Surface from Photographs of the Sun Glitter. Journal of the Optical Society of America. 1954, 44, 838–850.
2. Bakhanov V.V., Demakova A.A., Korinenko A.E., Ryabkova M.S., Titov V.I. Estimation of the wind wave spectra with centimeters-to-meter lengths by the sea surface images. Physical Oceanography [e-journal]. 2018, 25(3), 177–190. doi: 10.22449/1573-160X-2018-3-177-190
3. Kudryavtsev V., Yurovskaya M., Chapron B., Collard F., Donlon C. Sun glitter imagery of ocean surface waves. Part 1: Directional spectrum retrieval and validation. Journal of Geophysical Research: Oceans. 122, 2017, 2, 1369–1383.
4. doi: 10.1002/2016JC012425 4. Banner M.L., Jones S.F., Trinder J.C. Wavenumber spectra of short gravity waves. Journal of Fluid Mechanics. 1989, 198, 321–344. doi: 10.1017/S0022112089000157
5. Shemdin O.H., Tran H.M. Measuring Short Surface Waves with Stereography. Photogrammetric Engineering and Remote Sensing. 1992, 58, 311–316.
6. Benetazzo А. Measurements of short water waves using stereo matched image sequences. Coastal Engineering. 2006, 53, 12, 1013–1032. doi: 10.1016/j.coastaleng.2006.06.012
7. Kosnik M.V., Dulov V.A., Malinovskij V.V., Smolov V.E., Pogrebnoj A.E. Estimation of two-dimensional spatial spectra of short waves using stereo photography. Ekologicheskaya Bezopasnost’ Pribrezhnoj i Shel’fovoj Zon i Kompleksnoe Ispol’zovanie Resursov Shel’fa. 2009, 19, 401–414 (in Russian).
8. Molkov A.A., Dolin L.S. Determination of wind roughness characteristics based on an underwater image of the sea surface. Izvestiya. Atmospheric and Oceanic Physics. 2012, 48, 552–564. doi: 10.1134/S0001433812050088
9. Molkov A.A. Retrieval of slope spectrum of sea roughness by Snell’s window imagery: theory and numerical experiment (one-dimensional sea roughness). Proc. SPIE11529, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2020, 115290C. doi: 10.1117/12.2573949
10. Molkov A.A., Dolin L.S. Determination of the variance of rough water surface slopes be the distortions of Snell’s circle boundary. Izvestiya. Atmospheric and Oceanic Physics. 2013, 49 (5), 567—577. doi: 10.1134/S0001433813050071
11. Vieira G. d. S. et al. Stereo Vision Methods: From Development to the Evaluation of Disparity Maps. 2017 Workshop of Computer Vision (WVC). 2017, 132–137. doi: 10.1109/WVC.2017.00030
12. MatLab. Block Matching. URL: https://nl.mathworks.com/help/vision/ref/blockmatching.html (date of access: 31.08.2021).
13. MatLab. Semi-Global Matching. URL: https://nl.mathworks.com/help/vision/ref/disparitysgm.html (date of access: 31.08.2021).
14. Dolin L.S., Levin I.M. Theory of underwater vision. Leningrad, Gidrometeoizdat, 1991, 230 p. (in Russian).
15. Hartley R., Zisserman A. Multiple view geometry in computer vision. Cambridge University Press, 2004. 700 p. doi: 10.1017/S0263574700223217
Review
For citations:
Molkov A.A. Qualitative estimation of modeled Snell’s window stereo imagery for wind wave profile retrieval. Fundamental and Applied Hydrophysics. 2022;15(1):33-47. https://doi.org/10.59887/fpg/p112-6ptf-fh5r
Views:
177
Email this article 