Cartographic evaluation of image fusion methods when no precise registration is possible

REFERENCES

• Zhang, Y. and Hong, G. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images. Inform. Fusion, 2005, 6, 225–234.
   Google Scholar
• Ling, Y., Ehlers, M., Usery, E. L. and Madden, M. FFT-enhanced IHS transform method for fusing highresolution satellite images. ISPRS J. Photogramm. Remote Sens., 2007, 61, 381–392.
   Web of Science ®Google Scholar
• Núñez, J., Otazu, X., Fors, O., Prades, A., Pala, V. and Arbiol, R. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans. Geosci. Remote Sens., 1999, 37, 1204–1211.
   Web of Science ®Google Scholar
• Zhang, Y. A new merging method and its spectral and spatial effects. Int. J. Remote Sens., 1999, 20, 2003–2014.
   Web of Science ®Google Scholar
• Li, S., Kwok, J. T. and Wang, Y. Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inform. Fusion, 2002, 3, 17–23.
   Google Scholar
• Amolins, K., Zhang, Y. and Dare, P. Wavelet based image fusion techniques – an introduction, review and comparison. ISPRS J. Photogramm. Remote Sens., 2007, 62, 249–324.
   Web of Science ®Google Scholar
• Phol, C. and van Genderen, J. L. Multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens., 1998, 19, 823–854.
   Web of Science ®Google Scholar
• Carper, W. J., Lilesand, T. W. and Kieffer, R. W. The use of Intensity–hue–saturation transformation for merging SPOT panchromatic and multispectral image data. Photogramm. Eng. Remote Sens., 1990, 56, 459–467.
   Web of Science ®Google Scholar
• Chavez, P. S. and Kwarteng, A. Y. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogramm. Eng. Remote Sens., 1989, 55, 339–348.
   Web of Science ®Google Scholar
• Tu, T. M., Su, S. C., Shyu, H. C. and Huang, P. S. A new look at IHS-like image fusion methods. Inform. Fusion, 2001, 2, 177–186.
   Google Scholar
• Mallat, S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Patt. Anal. Mach. Intell., 1989, 11, 674–693.
   Web of Science ®Google Scholar
• Dutilleux, P. In Compt-rendus du Congres Ondelettes et Methods Temps – Frequence et Espace des Phases (Ed. J. Combes, A. Grossmann and Ph. Tchanitchian), 1987, pp. 298–304 (Springer-Verlag, Marseille).
   Google Scholar
• Lillo, M. and Gonzalo, C. Spectral or spatial quality for fused satellite imagery? A tradeoff solution using the wavelet à trous alhorithm. Int. J. Remote Sens., 2006, 27, 1453–1464.
   Google Scholar
• Nencini, F., Garzelli, A., Baronti, S. and Alparone, L. Remote sensing image fusion using the curvelet transform. Inform. Fusion, 2007, 8, 143–156.
   Web of Science ®Google Scholar
• Lillo, M. and Gonzalo, C. Multispectral images fusion by a joint multidirectional and multiresolution representation. Int. J. Remote Sens., 26, 1263–1268.
   Google Scholar
• Wald, L. Quality of high resolution synthesized images: is there a simple criterion?, Proc. 3rd Int. Conf. on Fusion of earth data, Sophia Antipolis, France, January 2000, SEE/URISCA, pp. 99–105.
   Google Scholar
• Wang, W., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 2004, 13, 600–612.
   PubMed Web of Science ®Google Scholar
• Zhou, J., Civco, D. and Silander, J. A wavelet method to merge Landsat TM and SPOT panchromatic data. Int. J. Remote Sens., 1998, 19, 743–757.
   Web of Science ®Google Scholar
• Prasad, N., Saran, S., Kushwaha, S. and Roy, P. Evaluation of various image fusion techniques and imaging scales for forest features interpretation. Curr. Sci., 2001, 81, 1218–1224.
   Web of Science ®Google Scholar
• Petrovic, V. Subjective tests for image fusion evaluation and objective metric validation. Inform. Fusion, 2007, 8, 208–216.
   Web of Science ®Google Scholar
• Toet, A. and Franken, E. M. Perceptual evaluation of different image fusion schemes. Displays, 2003, 24, 25–37.
   Web of Science ®Google Scholar
• Lakshmanan, V. A separable filter for directional smoothing. IEEE Geosci. Remote Sens. Lett., 2004, 1, 192–195.
   Web of Science ®Google Scholar
• BSI. ‘Photograpy – psychophysical experimental methods for estimating image quality – Part 1: Overview of psychophysical elements’, BSI BS ISO 20462–1, BSI, London, UK, 2006.
   Google Scholar