Abstract
The automatic feature extracting and matching for large amount of linear pushbroom imagery with higher and higher resolution is urgent and challenging in three dimensional reconstructions, remote sensing and mapping. Affine & scale-invariant heterogeneous pyramid feature is proposed in this paper, along with the new scale-invariant analysis method, the detecting of the key points, the affine & scale-invariant descriptor, the steering method of the matching, and the quasi-dense matching algorithm based on the extensive epipolar geometry. The automatic matching is devised for the linear pushbroom imagery. The whole process is executed on lunar images of the highest resolution of ~7 m/pixel (or ~1 m/pixel in the lower orbits) from the Chinese Chang’e 2 satellite, it runs robustly at present, and resulting in large amounts of well-distributed-correspondences with accuracy of 0.3 pixels and excellent reliability, which gives great support for the further exploration both on the Moon and the Earth.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References Cited
Cai, H. P., Lei, L., Su, Y., 2007. An Affine Invariant Region Detector Using the 4th Differential Invariant. ICTAI2007. 19th IEEE International Conference on IEEE, 1: 540–543. doi:10.1109/ICTAI.2007.112
Dawson, M. D., Todd, N. S., Lofgren, G., 2010. Integration of Apollo Lunar Sample Data into Google Moon. NASA USRP-Internship Final Report, Johnson Space Center. 1–8
Di, K. C., Yue, Z. Y., Liu, Z. Q., et al., 2013. Automated Rock Detection and Shape Analysis from Mars Rover Imagery and 3D Point Cloud Data. Journal of Earth Science, 24: 125–135
Gupta, R., Hartley, R. I., 1997. Linear Pushbroom Cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(9): 963–975. doi:10.1109/34.615446
Gwinner, K., Scholten, F., Spiegel, M., et al., 2009. Derivation and Validation of High-Resolution Digital Terrain Models from Mars Express HRSC-Data. Photogramm. Eng. Remote Sens., 75(9): 1127–1142
Heipke, C., Oberst, J., Albertz, J., et al., 2007. Evaluating Planetary Digital Terrain Models—The HRSC DTM Test. Planetary and Space Science, 55(14): 2173–2191
Jaumann, R., Neukum, G., Behnke, T., et al., 2007. The High-Resolution Stereo Camera (HRSC) Experiment on Mars Express: Instrument Aspects and Experiment Conduct from Interplanetary Cruise through the Nominal Mission. Planetary and Space Science, 55(7): 928–952
Ji, D. C., Li, X. J., Xie, J. W., 2008. A Hierarchical Aerial Photography Matching Algorithm. Journal of the Academy of Equipment Command & Technology, 4: 73–76 (in Chinese with English Abstract)
Juan, L., Gwun, O., 2009. A Comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing, 3(4): 143–152
Ke, Y., Sukthankar, R., 2004. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors//Computer Vision and Pattern Recognition, 2004 (CVPR 2004). Proceedings of the2004 IEEE Computer Society Conference on IEEE, 2: II-506-II-513
Kim, T., 2000. A Study on the Epipolarity of Linear Pushbroom Images. Photogrammetric Engineering and Remote Sensing, 66(8): 961–966
Kirk, R. L., Archinal, B. A., Gaddis, L. R., et al., 2008. Cartography for Lunar Exploration: 2008 Status and Mission Plans. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(B4): 1472–1489
Lee, H. Y., Park, W., 2002. A New Epipolarity Model Based on the Simplified Push-Broom Sensor Model. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(4): 631–636
Li, C. L., Liu, J. J., Ren, X., et al., 2010. The Global Image of the Moon by the Chang’e-1: Data Processing and Lunar Cartography. Sci. China Earth Sci., 3: 294–306 (in Chinese)
Li, J., Allinson, N. M., 2008. A Comprehensive Review of Current Local Features for Computer Vision. Neurocomputing, 71(10): 1771–1787
Lindeberg, T., 1994. Scale-Space Theory: A Basic Tool for Analyzing Strucures at Different Scale. Journal of Applied Statistics, 21: 225–270
Liu, F. J., Yang, R., Zhang, Y., et al., 2011. Distribution of Olivine and Pyroxene Derived from Clementine Data in Crater Copernicus. Journal of Earth Science, 22(5): 586–594
Lowe, D. G., 2004. Distinctive Image Features from Scale-Invariant Keypoints. Computer Vision, 2: 91–110
Mikolajczyk, K., Tuytelaars, T., Schmid, C., et al., 2005. A Comparison of Affine Region Detectors. International Journal of Computer Vision, 65(1–2): 43–72
Morgan, M. F., 2004. Epipolar Resampling of Linear Array Scanner Scenes: [Dissertation]. University of Calgary, Calgary
Shen, R. J., Li, X. J., 2010. Automatic Selenograph Production-New Direction of the Lunar Remote Sensor Data Processing. Journal of the Academy of Equipment Command & Technology: 1–5 (in Chinese with English Abstract)
Su, J. Y., 2002. Study of SPOT Epipolar Image Polynomial Fitting Based on Matching Constraint. Remote Sensing Information, 4: 10–15 (in Chinese with English Abstract)
Tuytelaars, T., Van, G. L., 2004. Matching Widely Separated Views Based on Affine Invariant Regions. International Journal of Computer Vision, 59(1): 61–85
Xie, J. R., Beigi, M. S., 2009. A Scale-Invariant Local Descriptor for Event Recognition in 1D Sensor Signals//Multimedia and Expo, ICME2009. IEEE International Conference on IEEE, June 28–July 3, 2009, New York. 1226–1229
Xu, W. Y., Huang, X. S., 2009. Analysis of Image Registration Technique for Recovering Large Rotation, Translation and Scale Parameters. Signal Processing, 25(10): 1598–1604 (in Chinese with English Abstract)
Yang, S., Li, X. J., 2011. Accelerating Matching Based on Improved SIFT for Selenograph. In: Proceedings of International Conference on Computer Design and Applications, Xi’an. 530–533
Yang, S., Li, X. J., Liu, T., et al., 2013a. A Review on Matching and Similarity Measures for High Resolution Remote Sensing Imagery. Geomatics & Spatial Information Technology, 36(5): 16–21 (in Chinese with English Abstract)
Yang, S., Li, X. J., Wang, J., et al., 2013b. Continuous Scale Multi-Change Detecting Quasi-Dense Matching for Epipolar Resample Images. Computer Technology and Development, 23(4): 111–114 (in Chinese with English Abstract)
Yang, S., Li, X. J., Xie, J. W., et al. 2012. Quasi-dense Matching of Selenograph Based on Image Waves. Proceedings of the 31st Chinese Control Conference, IEEE, Hefei. 5607–5612 (in Chinese with English Abstract)
Yokoya, N., 1994. Dense Matching of Two Views with Large Displacement//Image Processing,1994. Proceedings ICIP-94, IEEE International Conference, Nov. 13–16, 1994, Austin, TX.213–217
Zhang, G., Pan, H., Jiang, W., et al., 2010. Epipolar Resampling and Epipolar Geometry Reconstruction of Linear Array Scanner Scenes Based on RPC Model. Remote Sensing for Land & Resources, 4: 1–5 (in Chinese with English Abstract)
Zoltan, M., 2009. Dense Matching Methods for 3D Scene Reconstruction from Wide Baseline Images: [Dissertation]. Eotvos Lorand University, Budapest. 33–75
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, S., Zhu, S., Li, Z. et al. Affine & scale-invariant heterogeneous pyramid features for automatic matching of high resolution pushbroom imagery from Chang’e 2 satellite. J. Earth Sci. 27, 716–726 (2016). https://doi.org/10.1007/s12583-015-0605-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12583-015-0605-0