Hostname: page-component-848d4c4894-89wxm Total loading time: 0 Render date: 2024-07-06T04:10:58.772Z Has data issue: false hasContentIssue false

Real-time Terrain Matching Based on 3D Zernike Moments

Published online by Cambridge University Press:  26 July 2018

Kedong Wang*
Affiliation:
(School of Astronautics, Beihang University, Beijing 100191, China)
Tongqian Zhu
Affiliation:
(School of Astronautics, Beihang University, Beijing 100191, China)
Jinling Wang
Affiliation:
(School of Civil and Environmental Engineering, UNSW Australia, Sydney, NSW 2052, Australia)
*

Abstract

Since the descriptors based on Three-Dimensional (3D) Zernike moments are robust to geometric transformations and noise, they have been proposed for terrain matching. However, terrain matching algorithms based on 3D Zernike Moments (3DZMs) are often difficult to implement in practice since they are computationally intensive. This paper presents a more efficient real-time terrain matching algorithm based on 3DZMs for land applications. Two efficient methods based on coordinate transformation and symmetry are proposed to compute the geometric moments. The impact of the sample difference on the matching result due to heading angle is investigated to prove the feasibility of using a circular template. Consequently, the terrain feature vectors of the reference map can be computed off-line with the circular template to significantly reduce on-line computation. Numerical experiments on a real digital elevation model demonstrate that the proposed algorithm is robust to the heading angle and can be implemented for real-time terrain matching operations.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Al-Rawi, M. S. (2012). 3D (pseudo) Zernike moments: Fast computation via symmetry properties of spherical harmonics and recursive radial polynomials. Proceedings of IEEE International Conference on Image Processing, September 30-Octobor 3, 23532356.Google Scholar
Baraldi, A. and Parmiggiani, F. (1995). An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293304.Google Scholar
Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3): 346359.Google Scholar
Canterakis, N. (1997). Fast 3D Zernike moments and invariants. University of Freiburg.Google Scholar
Canterakis, N. (1999). 3D Zernike moments and Zernike affine invariants for 3D image analysis and recognition. Proceedings of 11th Scandinavian Conference on Image Analysis.Google Scholar
Choi, M. S. and Kim, W. Y. (2002). A novel two stage template matching method for rotation and illumination invariance. Pattern recognition, 35(1), 119129.Google Scholar
Clemente, C., Pallotta, L., De Maio, A., Soraghan, J. J. and Farina, A. (2015). A novel algorithm for radar classification based on Doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials. IEEE Transactions on Aerospace and Electronic Systems, 51(1), 417430.Google Scholar
Fountain, J. R. (1998). Digital terrain systems. Proceedings of IEE Colloquium on Airborne Navigation Systems, February 10.Google Scholar
Golden, J. P. (1980). Terrain contour matching (TERCOM): a cruise missile guidance aid. Proceedings of SPIE on Image Processing for Missile Guidance, 238, 1018.Google Scholar
Grandison, S., Roberts, C. and Morris, R. J. (2009). The application of 3D Zernike moments for the description of ‘model-free’ molecular structure, functional motion, and structural reliability. Journal of Computational Biology, 16(3), 487500.Google Scholar
Harris, C. and Stephens, M. (1988). A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference, 147151.Google Scholar
Hatamian, M. (1986). A real-time two-dimensional moment generating algorithm and its single chip implementation. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(3), 546553.Google Scholar
Hosny, K. M. and Hafez, M. A. (2012). An algorithm for fast computation of 3D Zernike moments for volumetric images. Mathematical Problems in Engineering.Google Scholar
Hostetler, L. (1978). Optimal terrain-aided navigation systems. Proceedings of the AIAA Guidance and Control Conference, 2030.Google Scholar
Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), 179187.Google Scholar
Keyes, L. and Winstanley, A. (2001). Data fusion for topographic object classification. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, 275279.Google Scholar
Khotanzad, A. and Hong, Y. H. (1990) Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5), 489497.Google Scholar
Kim, C. S., Cho, I. J., Lee, D. K. and Kang, I. J. (2015). Development of Low Altitude Terrain Following System based on Terrain profile matching. Journal of Institute of Control, Robotics and Systems, 21(9), 888897.Google Scholar
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91110.Google Scholar
Ma, Y. and Zhao, Y. (2012). Underwater terrain navigability analysis based on multi-beam data. IEEE 2012 Fifth International Joint Conference on Computational Sciences and Optimization, 848852.Google Scholar
Meduna, D. K., Rock, S. M., and McEwen, R. S. (2010). Closed-loop terrain relative navigation for AUVs with non-inertial grade navigation sensors. IEEE/OES Autonomous Underwater Vehicles, 8 pages.Google Scholar
Melo, J. and Matos, A. (2017). Survey on advances on terrain based navigation for autonomous underwater vehicles. Ocean Engineering, 139, 250264.Google Scholar
Millan, R. D., Dempere-Marco, L., Pozo, J. M., Cebral, J. R. and Frangi, A. F. (2007). Morphological characterization of intracranial aneurysms using 3-D moment invariants. IEEE transactions on medical imaging, 26(9), 12701282.Google Scholar
Novotni, M. and Klein, R. (2004). Shape retrieval using 3D Zernike descriptors. Computer-Aided Design, 36(11), 10471062.Google Scholar
Pozo, J. M., Villa-Uriol, M. C. and Frangi, A. F. (2011). Efficient 3D Geometric and Zernike moments computation from unstructured surface meshes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3), 471484.Google Scholar
Revaud, J., Lavoue, G. and Baskurt, A. (2009). Improving Zernike moments comparison for optimal similarity and rotation angle retrieval. IEEE transactions on pattern analysis and machine intelligence, 31(4), 627636.Google Scholar
Rodriguez, J. J. and Aggarwal, J. K. (1990). Matching aerial images to 3-D terrain maps. IEEE Transactions on pattern analysis and machine intelligence, 12(12), 11381149.Google Scholar
Sjanic, Z. and Gustafsson, F. (2015). Navigation and SAR focusing with map aiding. IEEE Transactions on Aerospace and Electronic Systems, 51(3), 16521663.Google Scholar
Song, Z. Q., Bian, H. Y. and Adam, Z. (2016). Application of acoustic image processing in underwater terrain aided navigation. Ocean Engineering, 121, 279290.Google Scholar
Tachikawa, T., Hato, M., Kaku, M. and Iwasaki, A. (2011). Characteristics of ASTER GDEM version 2. Proceedings of IEEE International Geoscience and remote sensing symposium (IGARSS), Vancouver, 36573660.Google Scholar
Venable, D. T. and Raquet, J. F. (2016). Large scale image aided navigation. IEEE Transactions on Aerospace and Electronic Systems, 52(6), 28492860.Google Scholar
Venkatraman, V., Chakravarthy, P. R. and Kihara, D. (2009). Application of 3D Zernike descriptors to shape-based ligand similarity searching. Journal of cheminformatics, 1(1), 19.Google Scholar
Wong, R. Y. and Hall, E. L. (1978). Sequential hierarchical scene matching. IEEE Transactions on Computers, (4), 359366.Google Scholar
Wu, Q. T. and Ye, B. (2008). 3D terrain matching algorithm and performance analysis based on 3D Zernike moments. Proceedings of International Conference on Computer Science and Software Engineering, December 12–14, 6: 7376.Google Scholar
Ye, B. and Chen, H. F. (2012, May). 3D Terrain Matching Algorithm Based on 3D Zernike Moments. Proceedings of IEEE Symposium on Photonics and Optoelectronics, May 21–23.Google Scholar