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Creative Commons License CC-BY-NC Статья опубликована с лицензией Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) – Лицензия «С указанием авторства – Некоммерческая».
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Tao Yu., Muller Jan-Peter A novel method for surface exploration: super-resolution restoration of mars repeat-pass orbital imagery

Аннотация: Higher resolution imaging data of planetary surfaces is considered desirable by the international community of planetary scientists interested in improving understanding of surface formation processes. However, given various physical constraints from the imaging instruments through to limited bandwidth of transmission one needs to trade-off spatial resolution against bandwidth. Even given optical communications, future imaging systems are unlikely to be able to resolve features smaller than 25 cm on most planetary bodies, such as Mars. In this paper, we propose a novel super-resolution restoration technique, called Gotcha-PDE-TV (GPT), taking advantage of the non-redundant sub-pixel information contained in multiple raw orbital images in order to restore higher resolution imagery. We demonstrate optimality of this technique in planetary image super-resolution restoration with example processing of 8 repeat-pass 25 cm HiRISE images covering the MER-A Spirit rover traverse in Gusev crater to resolve a 5 cm resolution of the area. We assess the “true” resolution of the 5 cm super-resolution restored images using contemporaneous rover Navcam imagery on the surface and an inter-comparison of landmarks in the two sets of imagery.

Ключевые слова:

Planetology, Gusev crater, Space Science, Rover, Super-resolution, HiRISE, epeat-pass, Orbital images, Mars, Planetary Surface.

Abstract: Higher resolution imaging data of planetary surfaces is considered desirable by the international community of planetary scientists interested in improving understanding of surface formation processes. However, given various physical constraints from the imaging instruments through to limited bandwidth of transmission one needs to trade-off spatial resolution against bandwidth. Even given optical communications, future imaging systems are unlikely to be able to resolve features smaller than 25 cm on most planetary bodies, such as Mars. In this paper, we propose a novel super-resolution restoration technique, called Gotcha-PDE-TV (GPT), taking advantage of the non-redundant sub-pixel information contained in multiple raw orbital images in order to restore higher resolution imagery. We demonstrate optimality of this technique in planetary image super-resolution restoration with example processing of 8 repeat-pass 25 cm HiRISE images covering the MER-A Spirit rover traverse in Gusev crater to resolve a 5 cm resolution of the area. We assess the “true” resolution of the 5 cm super-resolution restored images using contemporaneous rover Navcam imagery on the surface and an inter-comparison of landmarks in the two sets of imagery.

Keywords:

Planetology, Gusev crater, Space Science, Rover, Super-resolution, HiRISE, Repeat-pass, Orbital images, Mars, Planetary Surface


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Библиография
1. Sidiropoulos P., Muller J.-P. On the status of orbital high-resolution repeat imaging of mars for the observation of dynamic surface processes // Planet. Space Sci. (2015).
2. Shin D., Muller J.-P.Progressively weighted affine adaptive correlation matching for quasi-dense 3d reconstruction // Pattern Recognit., 45 (10) (2012), pp. 3795–3809.
3. Schultz R.R., Stevenson R.L. A Bayesian approach to image expansion for improved definition // IEEE Trans. Image Process., 3 (3) (1994), pp. 233–242.
4. Pock T., Unger M., Cremers D., Bischof H. Fast and exact solution of total variation models on the gpu // CVPR Workshop on Visual Computer Vision on GPUs, 2008.
5. Park S., Lee J., Lee H., Shin J., Seo J., Lee K.H., Shin Y.-G., Kim B.Parallelized seeded region growing using cuda // Comput. Math. Methods Med., 2014 (2014), p. 10.
6. Morley J., Sprinks J., Muller J.-P., Tao,Y., Paar G., Huber B., Bauer A., Willner K., Traxler C., Garov A., Karachevtseva I. Contextualising and analysing planetary rover image products through the web-based progis // Proceedings of Geophysical Research Abstracts (EGU), 2014, vol. 16.
7. Kim J.-R., Muller J.-P. Multi-resolution topographic data extraction from martian stereo imagery // Planet. Space Sci., 57 (2009), pp. 2095–2112.
8. Irani M., Peleg S. Motion analysis for image enhancement: resolution, occlusion and transparency // J. Vis. Commun. Image Represent., 4 (4) (1993), pp. 324–335.
9. Kaltenbacher, E., Hardie, R.C. High resolution infrared image reconstruction using multiple low resolution aliased frames // IEEE National Aerospace Electronics Conference, 1996, 2, pp. 702–709.
10. Heymann S., Frhlich B., Medien F., Mller K., Wiegand T., 2007. Sift implementation and optimization for general-purpose gpu. In: Proceedings of WSCG 07.
11. Herman G.T., Hurwitz H., Lent A., Lung H.-P. On the Bayesian approach to image reconstruction // Inf. Control, 42 (1) (1979), pp. 60–71.
12. Golombek M., Grant J., Kipp D., Vasavada A., Kirk R., Fergason R., et al.Selection of the mars science laboratory landing site // Space Sci. Rev., 170 (1–4) (2012), pp. 641–737.
13. Farsiu S., Robinson D., Elad M., Milanfar P. Fast and robust multi-frame super-resolution // IEEE Trans. Image Process., 13 (10) (2004), pp. 1327–1344.
14. Bouzari H. An improved regularization method for artifact rejection in image super-resolution // Signal Image Video Process., 6 (2012), pp. 125–140.
15. Capel D. Image Mosaicing and Super-resolution, 2004, Springer-Verlag, London Ltd., eBook, ISBN 978-0-85729-384-8 (Springer-Verlag, London, Berlin, Heidelberg).
16. Tao Y., Muller J.-P. Automated science target selection for future mars rovers: a machine vision approach for the future esa exomars 2018 rover mission. In: Proceedings of Geophysical Research Abstracts (EGU), 2013, vol. 15.
17. Tao Y., Muller J.-P., Willner K., Morley J., Sprinks J., Traxler C., Paar G. 3d data products and web-gis for mars rover missions for seamless visualisation from orbit to ground-level. In: Proceedings of ISPRS Commission IV Symposium, 2014, vol. 8.
18. Tsai R.Y., Huang T.S. Multiple frame image restoration and registration // Adv. Comput. Vis. Image Process. (1984), pp. 317–339.
References
1. Sidiropoulos P., Muller J.-P. On the status of orbital high-resolution repeat imaging of mars for the observation of dynamic surface processes // Planet. Space Sci. (2015).
2. Shin D., Muller J.-P.Progressively weighted affine adaptive correlation matching for quasi-dense 3d reconstruction // Pattern Recognit., 45 (10) (2012), pp. 3795–3809.
3. Schultz R.R., Stevenson R.L. A Bayesian approach to image expansion for improved definition // IEEE Trans. Image Process., 3 (3) (1994), pp. 233–242.
4. Pock T., Unger M., Cremers D., Bischof H. Fast and exact solution of total variation models on the gpu // CVPR Workshop on Visual Computer Vision on GPUs, 2008.
5. Park S., Lee J., Lee H., Shin J., Seo J., Lee K.H., Shin Y.-G., Kim B.Parallelized seeded region growing using cuda // Comput. Math. Methods Med., 2014 (2014), p. 10.
6. Morley J., Sprinks J., Muller J.-P., Tao,Y., Paar G., Huber B., Bauer A., Willner K., Traxler C., Garov A., Karachevtseva I. Contextualising and analysing planetary rover image products through the web-based progis // Proceedings of Geophysical Research Abstracts (EGU), 2014, vol. 16.
7. Kim J.-R., Muller J.-P. Multi-resolution topographic data extraction from martian stereo imagery // Planet. Space Sci., 57 (2009), pp. 2095–2112.
8. Irani M., Peleg S. Motion analysis for image enhancement: resolution, occlusion and transparency // J. Vis. Commun. Image Represent., 4 (4) (1993), pp. 324–335.
9. Kaltenbacher, E., Hardie, R.C. High resolution infrared image reconstruction using multiple low resolution aliased frames // IEEE National Aerospace Electronics Conference, 1996, 2, pp. 702–709.
10. Heymann S., Frhlich B., Medien F., Mller K., Wiegand T., 2007. Sift implementation and optimization for general-purpose gpu. In: Proceedings of WSCG 07.
11. Herman G.T., Hurwitz H., Lent A., Lung H.-P. On the Bayesian approach to image reconstruction // Inf. Control, 42 (1) (1979), pp. 60–71.
12. Golombek M., Grant J., Kipp D., Vasavada A., Kirk R., Fergason R., et al.Selection of the mars science laboratory landing site // Space Sci. Rev., 170 (1–4) (2012), pp. 641–737.
13. Farsiu S., Robinson D., Elad M., Milanfar P. Fast and robust multi-frame super-resolution // IEEE Trans. Image Process., 13 (10) (2004), pp. 1327–1344.
14. Bouzari H. An improved regularization method for artifact rejection in image super-resolution // Signal Image Video Process., 6 (2012), pp. 125–140.
15. Capel D. Image Mosaicing and Super-resolution, 2004, Springer-Verlag, London Ltd., eBook, ISBN 978-0-85729-384-8 (Springer-Verlag, London, Berlin, Heidelberg).
16. Tao Y., Muller J.-P. Automated science target selection for future mars rovers: a machine vision approach for the future esa exomars 2018 rover mission. In: Proceedings of Geophysical Research Abstracts (EGU), 2013, vol. 15.
17. Tao Y., Muller J.-P., Willner K., Morley J., Sprinks J., Traxler C., Paar G. 3d data products and web-gis for mars rover missions for seamless visualisation from orbit to ground-level. In: Proceedings of ISPRS Commission IV Symposium, 2014, vol. 8.
18. Tsai R.Y., Huang T.S. Multiple frame image restoration and registration // Adv. Comput. Vis. Image Process. (1984), pp. 317–339.
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