Suma Dawn
ABSTRACT
DEM (Digital Elevation Model) is the model that gives elevation of each point of the earth surface in discrete form in a 3-D space. Image registration, implies registration of multi-temporal, multi-modal, multi-resolution, images of the same area. Registration of DEMs is now a days gaining a lot of popularity among the research community. DEM registration, in this case, is the registration of multi-temporal DEMs. Popular techniques for feature extraction and matching include wavelet approach, robust SIFT, or are based "super-points". This paper presents the DEM registration scheme based on watershed transformation, followed by two post-processing steps of clustering and morphological operations which is applied on both the DEMs -- candidate, as well as, reference DEM. Chain coding based matching is concluding step of the complete process. The system is semi-automatic i. e. expert input is considered before the final registration. Experimental results give good outcomes as shown from the error matrix analysis of RMSE, and PSNR. The system may be extended by using fuzzy classification and context-sensitive learning.
- Beucher, S. 1991. The Watershed Transform Applied to Image Segmentation. Proceedings of the Pfefferkorn Conference on Signal and Image Processing in Microscopy and Microanalysis. (Sep. 1991), 299--314.Google Scholar
- Bhagwat, M., Krishna R. K., and Pise, V. 2010. Simplified Watershed Transformation. International Journal of Computer Science & Communication. 1, 1, 175--177.Google Scholar
- Derivaux, S., Forestier, G., Wemmert, C., and Lefever, S. 2010. Supervised Image Segmentation Using Watershed Transform, Fuzzy Classification and Evolutionary Computation. Pattern Recognition Letters, 31, 15, 2364--2374. Google Scholar
- Galile, B., Mamalet, F., Renaudin, M., and Coulon, P. 2007. Parallel Asynchronous Watershed Algorithm-Architecture. IEEE Transactions on Parallel and Distributed Systems, 18, 1, (Jan 2007), 44--56. Google Scholar
- Hamarneh, G. and Li, X. 2009. Watershed segmentation using prior shape and appearance knowledge. Image and Vision Computing, 27, 59--68. Google Scholar
- Kayadibi, O. 2009. Recent advances in satellite technologies using to generate the Digital Elevation Model (DEM). 4<sup>th</sup> International Conference on Recent Advances in Space Technologies, 380--385. DOI: 10.1109/RAST.2009.5158229.Google Scholar
- Yao, L. and Chen, H. 2002. The research on the displaying methods of visual digital terrain based on DEM data. Proceedings of the 4th World Congress on Intelligent Control and Automation, 1, 134--138, DOI: 10.1109/WCICA.2002.1022084.Google Scholar
- Maire, C. and Datcu, M. 2005. Earth observation image and DEM information aggregation for realistic 3-D visualization of natural landscapes. IEEE Transactions on Geoscience and Remote Sensing. 43, 11, 2676--2683.Google Scholar
- Sole, A., Caselles, V., Sapiro, G. and Arandiga, F. 2003 Morse description and geometric encoding of DEM data. Proceedings of International Conference on Image Processing, 13, 9, 1245--1262.Google Scholar
- Hai, S., Cheng, W. and Bo, R. 2008. Method of DEM Data's Processing in Flood Simulation System. International Conference on Computer Science and Software Engineering. 6, 50--53. DOI: 10.1109/CSSE.2008.706. Google Scholar
- Trisakti, B. and Carolita, I. Comparison result of DEM generated from ASTER Stereo Data and SRTM. Available: http://www.gisdevelopment.net/technology/tm/tm001b.htmGoogle Scholar
- Rodriguez, J. and Ayala, D. 2001. Erosion and Dilation on 2D and 3D Digital Images: A new size-independent approach. 6th International Fall Workshop on Vision, Modeling, and Visualization. Stuttgart, Germany, (Nov. 2001). Google Scholar
- Number: UMI Order No. GAX95-09398., University of Washington.Google Scholar
- Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289--1305. Google Scholar
- Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE. In Proceedings of the 16th Annual ACM Symposium on User Interface Software and Technology (Vancouver, Canada, November 02--05, 2003). UIST '03. ACM, New York, NY, 1-10. DOI= http://doi.acm.org/10.1145/964696.964697. Google Scholar
- Yu, Y. T. and Lau, M. F. 2006. A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions. J. Syst. Softw. 79, 5 (May. 2006), 577--590. DOI= http://dx.doi.org/10.1016/j.jss.2005.05.030. Google Scholar
- Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender, Ed. ACM Press Frontier Series. ACM, New York, NY, 19--33. DOI= http://doi.acm.org/10.1145/90417.90738. Google Scholar
Index Terms
- DEM registration using watershed algorithm and chain coding
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