ADAPTIVE CHEBYSHEV FUSION OF VEGETATION IMAGERY BASED ON SVM CLASSIFIER
DOI:
https://doi.org/10.11113/jt.v78.9175Keywords:
Image fusion, Chebyshev polynomials, remote sensingAbstract
A novel adaptive image fusion method by using Chebyshev polynomial analysis (CPA), for applications in vegetation satellite imagery, is introduced in this paper. Fusion is a technique that enables the merging of two satellite cameras: panchromatic and multi-spectral, to produce higher quality satellite images to address agricurtural and vegetation issues such as soiling, floods and crop harvesting. Recent studies show Chebyshev polynomials to be effective in image fusion mainly in medium to high noise conditions, as per real-life satellite conditions. However, its application was limited to heuristics. In this research, we have proposed a way to adaptively select the optimal CPA parameters according to user specifications. Support vector machines (SVM) is used as a classifying tool to estimate the noise parameters, from which the appropriate CPA degree is utilised to perform image fusion according to a look-up table. Performance evaluation affirms the approach’s ability in reducing the computational complexity to perform fusion. Overall, adaptive CPA fusion is able to optimize an image fusion system’s resources and processing time. It therefore may be suitably incorporated onto real hardware for use on vegetation satellite imagery.  Â
References
J. A. Williams. 1992. Vegetation Classification Using Landsat TM and SPOT-HRV Imagery in Mountainous Terrain, Kananaskis Country, S.W. Alberta. Research Study, Alberta Recreation and Parks, Kananaskis Country Operations Branch, Environmental Management, Canmore, Alberta.
G. A. Carpenter, M. N. Gjaja, S. Gopal, C. E. Woodcock. 1997. ART Neural Networks For Remote Sensing: Vegetation Classification From Landsat TM And Terrain Data. Geoscience and Remote Sensing, IEEE Transactions on. 35(2): 308-325.
Z. Omar. 2012. Signal Processing Algorithms for Enhanced Image Fusion Performance and Assessment. Ph.D Thesis. Department of Electrical and Electronic Engineering, Imperial College London.
T. Stathaki (Ed.). 2008. Image Fusion: Algorithms and Applications. Academic Press.
Z. Omar, N. Mitianoudis and T. Stathaki. 2010. Two-dimensional Chebyshev Polynomials for Image Fusion. 28th Picture Coding Symposium, Japan. 426-429.
http://www.ucalgary.ca/GEOG/Virtual/RemoteSensing/rsveg.html, accessed on 10 January 2014.
F. Calderero, F. Marques, J. Marcello, F. Eugenio. 2009. Hierarchical Segmentation Of Vegetation Areas In High Spatial Resolution Images By Fusion Of Multispectral Information. Geoscience and Remote Sensing Symposium, 2009 IEEE International,IGARSS. 200: IV-232,IV-235.
C. Pohl. 2013. Remote Sensing Image Fusion: An Update In The Context Of Digital Earth. International Journal of Digital Earth. Taylor & Francis Online,
G. Simone, A. Farina, F.C. Morabito, S.B. Serpico and L. Bruzzone. 2002. Image Fusion Techniques For Remote Sensing Applications. Information Fusion 3. 3-15.
E. Basaeed, H. Bhaskar, M. Al-Mualla. 2013. Comparative Analysis Of Pan-Sharpening Techniques on DubaiSat-1 Images. Information Fusion (FUSION), 2013 16th International Conference on. 227-234.
Z. Wang, D. Ziou, C. Armenakis, D. Li and Q. Li. 2005. A Comparative Analysis Of Image Fusion Methods. IEEE Transactions on Geoscience and Remote Sensing. 43(6): 1391-1402.
F. Nencini, A. Garzelli, S. Baronti and L. Alparone. 2007. Remote Sensing Image Fusion Using The Curvelet Transform. Information Fusion 8. 143-156.
Z. Omar, N. Mitianoudis and T. Stathaki. 2011. Region-based Image Fusion Using A Combinatory Chebyshev-ICA Method. Proc. Intl. Conf. on Acoustics, Speech and Signal Processing, Prague. 1213-1216.
J. C. Mason and D. C. Handscomb. 2003. Chebyshev Polynomials. Chapman & Hall/CRC, Florida. 105-141.
N. Amthul. 2009. Image Fusion Using Two Dimensional Chebyshev Polynomials. MSc Dissertation, Imperial College London.
C. S. Xydeas and V. Petrovic. 2000. Objective Image Fusion Performance Measure. Electronics Letters. 36(4): 308-309.
C. Cortes, V. Vapnik. 1995. Support-vector Networks. Machine Learning. 20(4): 273.
H. William H., S. A. Teukolsky, W. T. Vetterling, B. P. Flannery. 2007. Section 16.5. Support Vector Machines. Numerical Recipes: The Art of Scientific Computing. 3rd Ed. Cambridge University Press,
Shahdoosti, H. R., Ghassemian, H. 2015. Fusion of MS and PAN Images Preserving Spectral Quality. Geoscience and Remote Sensing Letters, IEEE. 12(3): 611-615.
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