A Novel Image Fusion System for Multisensor and Multiband Remote Sensing Data

REFERENCES

• L. Wald, “Some terms of reference in data fusion,” IEEE Trans. Geosci. Remote Sens., Vol. 37, pp. 1190–3, May 1999.
   Web of Science ®Google Scholar
• George Joseph, Fundamentals of Remote Sensing. Hyderabad: University Press (India) Private Limited, 2007.
   Google Scholar
• Gintautas Palubinskas, Peter Reinartz, and Richard Bamler, “Image acquisition geometry analysis for the fusion of optical and radar remote sensing data,” Int. J. Image. Data Fusion, Vol. 1, no. 3, pp. 271–82, Sep. 2010.
   Google Scholar
• C. Pohl, and J. L. Van Genderen, “Multisensor image fusion in remote sensing: Concepts, methods and applications,” Int. J. Remote Sens., Vol. 19, no. 5, pp. 823–54, Mar. 1998.
   Web of Science ®Google Scholar
• T. Ranchin, and L. Wald, “Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation,” Photogramm. Eng. Remote Sens., Vol. 66, no. 1, pp. 4–18, Jan. 2000.
   Google Scholar
• K. A. Kalpoma, and J. Kudoh, “Image fusion processing for IKONOS 1-m color imagery,” IEEE Trans. Geosci. Remote Sens., Vol. 45, no. 10, pp. 3075–86, Oct. 2007.
   Web of Science ®Google Scholar
• Y. Ling, M. Ehlers, E. L. Usery, and M. Madden, “Effects of spatial resolution ratio in image fusion,” Int. J. Remote Sens., Vol. 29, no. 7, pp. 2157–67, Apr. 2008.
   Web of Science ®Google Scholar
• P. Suresh Kumar, R. Chandrakanth, and R. Ramachandran, “MRF based speckle filters,” in Symposium on Advances in Microwave Remote Sensing Applications, CSRE, IIT Mumbai, India, 2003.
   Google Scholar
• Chris Oliver, and Shaun Quegan, Understanding Synthetic Aperture Radar Images. Boston, MA: Artech House, 1998, ch. 6.
   Google Scholar
• S. Baronti, B. Aiazzi, M. Selva, A. Garzelli, and L. Alparone, “A theoretical analysis of the effects of aliasing and misregistration on pansharpened imagery,” IEEE J. Sel. Top. Signal Proc., Vol. 5, no. 3, pp. 446–53, Jun. 2011.
   Google Scholar
• P. Suresh kumar, R. Chandrakanth, Neeraj Mishra, J. Saibaba, and Geeta Varadan, “A generalized search scheme for automatic registration of remote-sensing data,” Int. J. Remote Sens., Vol. 33, no. 2, pp. 490–501, Jan. 2012.
   Google Scholar
• F. Wang, B. C. Vemuri, M. Rao, and Y. Chen, “Cumulative residual entropy, a new measure of information and its application to image alignment,” in Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV), Nice, France, Oct. 2003 , pp. 548--53.
   Google Scholar
• L. Zagorchev, and A. Goshtasby, “A comparative study of transformation functions for nonrigid image registration,” IEEE Trans. Image Proc., Vol. 15, no. 3, pp. 529–38, Mar. 2006.
   PubMed Web of Science ®Google Scholar
• A. Svab, and K. Ostir, “High-resolution image fusion: Methods to preserve spectral and spatial resolution,” Photogramm. Eng. Remote Sens., Vol. 72, no. 5, pp. 565–72, May 2006.
   Google Scholar
• W. J. Carper, T. M. Lillesand, and R. W. Kiefer, “The use of intensity-hue-saturation transform for merging SPOT panchromatic and multispectral image data,” Photogramm. Eng. Remote Sens., Vol. 56, pp. 459–67, Apr. 1990.
   Web of Science ®Google Scholar
• Z. Wang, D. Ziou, C. Armenakis, D. Li, and Q. Li, “A comparative analysis of image fusion methods,” IEEE Trans. Geosci. Remote Sens., Vol. 43, no. 6, pp. 1391–402, Jun. 2005.
   Web of Science ®Google Scholar
• M. Baisantry, D. S. Negi, O. P. Manocha, B. P. Singh, and Sandeep Kishore, “Object-based image fusion for minimization of color distortion in high resolution satellite imagery,” in Proceedings of the 2011 International Conference on Image Information Processing (ICIIP 2011), Shimla, India, 2011, pp. 1–6.
   Google Scholar
• P. J. Burt, and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun., Vol. 31, pp. 532–40, Apr. 1983.
   Web of Science ®Google Scholar
• E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, “Pyramid methods in image processing,” RCA Engineer, Vol. 29–6, pp. 33–41, Nov. /Dec. 1984.
   Google Scholar
• S. Klonus, “Comparison of pansharpening algorithms for combining radar and multispectral data,” in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B6b, Beijing, 2008, pp. 189–94.
   Google Scholar
• D. Amarsaikhana, H. H. Blotevogelb, J. L. van Genderenc, M. Ganzoriga, R. Gantuyaa, and B. Nerguia, “Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification,” Int. J. Image Data Fusion, Vol. 1, no. 1, pp. 83–97, Feb. 2010.
   Google Scholar
• A. Toet, J. J. Van Ruyven, and J. M. Valeton, “Merging thermal and visual images by a contrast pyramid,” Opt. Eng., Vol. 28, no. 7, pp. 789–92, Jul. 1989.
   Web of Science ®Google Scholar
• P. J. Burt, and R. J. Kolczynski, “Enhanced image capture through fusion,” in Proceedings of the 4^th IEEE International Conference on Computer Vision (ICCV ’93), Berlin, 1993, pp. 173–82.
   Google Scholar
• T. A. Wilson, S. K. Rogers, and M. Kabrisky, “Perceptual-based image fusion for hyperspectral data,” IEEE Trans. Geosci. Remote Sens., Vol. 35, pp. 1007–17, Jul. 1997.
   Web of Science ®Google Scholar
• R. Chandraknath, J. Saibaba, Geeta Varadan, and P Ananthraj, “Feasibility of high resolution SAR and multispectral data fusion,” in IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, Canada, Jul. 2011, pp. 356–9.
   Google Scholar
• R. Chandrakanth, J. Saibaba, Geeta Varadan, and P. Ananthraj, “Fusion of high resolution satellite SAR and optical images”, in International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM), Xiamen, China, 2011, pp. 1–6.
   Google Scholar
• R. Guo, L. Zhang, M. Xing, and J. Li, “Polarimetric SAR image fusion using nonnegative matrix factorisation and improved-RGB model,” Electron. Lett., Vol. 46, no. 20, Sep. 2010.
   Google Scholar
• J. Zhang, J. Wei, G. Huang, and Y. Zhang, “Fusion of ascending and descending polarimetric SAR data for colour orthophoto generation,” in ISPRS TC VII Symposium – 100 Years, Vienna, Austria, Jul. 2010, pp. 323–8.
   Google Scholar