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Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing (2401.01175v1)

Published 2 Jan 2024 in cs.CV

Abstract: Simulating high-resolution Synthetic Aperture Radar (SAR) images in complex scenes has consistently presented a significant research challenge. The development of a microwave-domain surface scattering model and its reversibility are poised to play a pivotal role in enhancing the authenticity of SAR image simulations and facilitating the reconstruction of target parameters. Drawing inspiration from the field of computer graphics, this paper proposes a surface microwave rendering model that comprehensively considers both Specular and Diffuse contributions. The model is analytically represented by the coherent spatially varying bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the perturbation method (SPM). And SAR imaging is achieved through the synergistic combination of ray tracing and fast mapping projection techniques. Furthermore, a differentiable ray tracing (DRT) engine based on SAR images was constructed for CSVBSDF surface scattering parameter learning. Within this SAR image simulation engine, the use of differentiable reverse ray tracing enables the rapid estimation of parameter gradients from SAR images. The effectiveness of this approach has been validated through simulations and comparisons with real SAR images. By learning the surface scattering parameters, substantial enhancements in SAR image simulation performance under various observation conditions have been demonstrated.

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References (36)
  1. K. Yee, “Numerical solution of initial boundary value problems involving maxwell’s equations in isotropic media,” IEEE Transactions on antennas and propagation, vol. 14, no. 3, pp. 302–307, 1966.
  2. D. S. Weile, G. Pisharody, N.-W. Chen, B. Shanker, and E. Michielssen, “A novel scheme for the solution of the time-domain integral equations of electromagnetics,” IEEE transactions on Antennas and Propagation, vol. 52, no. 1, pp. 283–295, 2004.
  3. P. Beckmann and A. Spizzichino, “The scattering of electromagnetic waves from rough surfaces,” Norwood, 1987.
  4. W.-C. Wang, “Electromagnetic wave theory,” Google Scholar, 1986.
  5. A. K. Fung, Z. Li, and K.-S. Chen, “Backscattering from a randomly rough dielectric surface,” IEEE Transactions on Geoscience and remote sensing, vol. 30, no. 2, pp. 356–369, 1992.
  6. R. G. Kouyoumjian, “Asymptotic high-frequency methods,” Proceedings of the IEEE, vol. 53, no. 8, pp. 864–876, 1965.
  7. P. Y. Ufimtsev, “Elementary edge waves and the physical theory of diffraction,” Electromagnetics, vol. 11, no. 2, pp. 125–160, 1991.
  8. J. B. Keller, “Geometrical theory of diffraction,” Josa, vol. 52, no. 2, pp. 116–130, 1962.
  9. R. G. Kouyoumjian and P. H. Pathak, “A uniform geometrical theory of diffraction for an edge in a perfectly conducting surface,” Proceedings of the IEEE, vol. 62, no. 11, pp. 1448–1461, 1974.
  10. G. A. Deschamps, “Ray techniques in electromagnetics,” Proceedings of the IEEE, vol. 60, no. 9, pp. 1022–1035, 1972.
  11. H. Ling, R.-C. Chou, and S.-W. Lee, “Shooting and bouncing rays: Calculating the rcs of an arbitrarily shaped cavity,” IEEE Transactions on Antennas and propagation, vol. 37, no. 2, pp. 194–205, 1989.
  12. J. M. Norman, J. M. Welles, and E. A. Walter, “Contrasts among bidirectional reflectance of leaves, canopies, and soils,” IEEE Transactions on Geoscience and Remote Sensing, no. 5, pp. 659–667, 1985.
  13. K. Tomiyasu, “Relationship between and measurement of differential scattering coefficient (sigma/sup 0/) and bidirectional reflectance distribution function (brdf),” IEEE Transactions on Geoscience and Remote Sensing, vol. 26, no. 5, pp. 660–665, 1988.
  14. R. J. Woodham and M. H. Gray, “An analytic method for radiometric correction of satellite multispectral scanner data,” IEEE Transactions on Geoscience and Remote Sensing, no. 3, pp. 258–271, 1987.
  15. Y. Chang, Z. Jiao, X. Zhang, L. Mei, Y. Dong, S. Yin, L. Cui, A. Ding, J. Guo, R. Xie et al., “Assessment of improved ross–li brdf models emphasizing albedo estimates at large solar angles using polder data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 12, pp. 9968–9986, 2020.
  16. S. Liang and A. H. Strahler, “An analytic brdf model of canopy radiative transfer and its inversion,” IEEE transactions on geoscience and remote sensing, vol. 31, no. 5, pp. 1081–1092, 1993.
  17. X. Zhang and F. Xu, “Coherent spatially varying bidirectional scattering distribution function of rough surface,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–17, 2021.
  18. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020.
  19. J. Guo, B. Lei, C. Ding, and Y. Zhang, “Synthetic aperture radar image synthesis by using generative adversarial nets,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 7, pp. 1111–1115, 2017.
  20. Z. Cui, M. Zhang, Z. Cao, and C. Cao, “Image data augmentation for sar sensor via generative adversarial nets,” IEEE Access, vol. 7, pp. 42 255–42 268, 2019.
  21. Q. Guo and F. Xu, “Interpretable disentangled adversarial auto-encoder for sar-atr with sparse training samples,” in IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium.   IEEE, 2023, pp. 7511–7514.
  22. M. M. Loper and M. J. Black, “Opendr: An approximate differentiable renderer,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VII 13.   Springer, 2014, pp. 154–169.
  23. S. Liu, T. Li, W. Chen, and H. Li, “Soft rasterizer: A differentiable renderer for image-based 3d reasoning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 7708–7717.
  24. T.-M. Li, M. Aittala, F. Durand, and J. Lehtinen, “Differentiable monte carlo ray tracing through edge sampling,” ACM Transactions on Graphics (TOG), vol. 37, no. 6, pp. 1–11, 2018.
  25. P. Henderson and V. Ferrari, “Learning single-image 3d reconstruction by generative modelling of shape, pose and shading,” International Journal of Computer Vision, vol. 128, no. 4, pp. 835–854, 2020.
  26. W. Chen, H. Ling, J. Gao, E. Smith, J. Lehtinen, A. Jacobson, and S. Fidler, “Learning to predict 3d objects with an interpolation-based differentiable renderer,” Advances in neural information processing systems, vol. 32, 2019.
  27. F. Xu and Y.-Q. Jin, “Bidirectional analytic ray tracing for fast computation of composite scattering from electric-large target over a randomly rough surface,” IEEE Transactions on Antennas and Propagation, vol. 57, no. 5, pp. 1495–1505, 2009.
  28. E. Pottier, L. Ferro-Famil, S. Allain, S. Cloude, I. Hajnsek, K. Papathanassiou, A. Moreira, M. Williams, A. Minchella, M. Lavalle et al., “Overview of the polsarpro v4. 0 software. the open source toolbox for polarimetric and interferometric polarimetric sar data processing,” in 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 4.   IEEE, 2009, pp. IV–936.
  29. S. J. Auer, “3d synthetic aperture radar simulation for interpreting complex urban reflection scenarios,” Ph.D. dissertation, Technische Universität München, 2011.
  30. F. Xu and Y.-Q. Jin, “Imaging simulation of polarimetric sar for a comprehensive terrain scene using the mapping and projection algorithm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 11, pp. 3219–3234, 2006.
  31. S. Fu and F. Xu, “Differentiable sar renderer and image-based target reconstruction,” IEEE Transactions on Image Processing, vol. 31, pp. 6679–6693, 2022.
  32. B. T. Phong, “Illumination for computer generated pictures,” in Seminal graphics: pioneering efforts that shaped the field, 1998, pp. 95–101.
  33. Z. Dong, B. Walter, S. Marschner, and D. P. Greenberg, “Predicting appearance from measured microgeometry of metal surfaces,” ACM Transactions on Graphics (TOG), vol. 35, no. 1, pp. 1–13, 2015.
  34. L.-Q. Yan, M. Hašan, B. Walter, S. Marschner, and R. Ramamoorthi, “Rendering specular microgeometry with wave optics,” ACM Transactions on Graphics (TOG), vol. 37, no. 4, pp. 1–10, 2018.
  35. B. Karis and E. Games, “Real shading in unreal engine 4,” Proc. Physically Based Shading Theory Practice, vol. 4, no. 3, p. 1, 2013.
  36. S. G. Parker, J. Bigler, A. Dietrich, H. Friedrich, J. Hoberock, D. Luebke, D. McAllister, M. McGuire, K. Morley, A. Robison et al., “Optix: a general purpose ray tracing engine,” Acm transactions on graphics (tog), vol. 29, no. 4, pp. 1–13, 2010.

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