Fusion and super-resolution in multispectral data using neural networks for improved RGB representation

References

• Richards, J. A. and Jia, X. Remote Sensing Digital Image Analysis, 1999, 3rd edn (Springer, Berlin).
   Google Scholar
• Lehmann, T. M., Gonner, C. and Spitzer, K. Survey: interpolation methods in medical image processing. IEEE Trans. Med. Imag., 1999, 18(11), 1049–1075.
   PubMed Web of Science ®Google Scholar
• Chaudhuri, S. Super-resolution Imaging, 2001 (Kluwer Academic, Norwell, MA).
   Google Scholar
• Park, S. C., Park, M. K. and Kang, M. G. Superresolution image reconstruction: a technical overview. IEEE Sign. Process. Mag., 2003, 20(3), 21–36.
   Web of Science ®Google Scholar
• Go, J., Sohn, K. and Lee, Ch. Interpolation using neural networks for digital still cameras. IEEE Trans. Cons. Electr.., 2000, 46(3), 610–616.
   Web of Science ®Google Scholar
• Pei, S. C. and Tam, I. K. Effective color interpolation in CCD color filter array using signal correlation. IEEE Trans. Circ. Syst. Video Technol., 2003, 13(6), 53–513.
   Google Scholar
• Gunturk, B. K., Altunbasak, Y. and Mersereau, R. M. Color plane interpolation using alternating projections. IEEE Trans. Image Process., 2002, 11(9), 997–1013.
   PubMed Web of Science ®Google Scholar
• Borman, S. and Stevenson, R. L. Super-resolution from image sequences—a review, IEEE Proc. 1998 Midwest Symp. on Circuits and Systems, South Bend, IN, 1999, pp. 374–378.
   Google Scholar
• Baker, S. W. and Kanade, T. Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell, 2002, 9, 1167–1183.
   Web of Science ®Google Scholar
• Valdes, M. D. C. and Inamura, M. Spatial resolution improvement of remotely sensed images by a fully interconnected neural network approach. IEEE Trans. Geosci. Remote Sens., 2000, 38(5), 2426–2430.
   Web of Science ®Google Scholar
• Tatem, A. J., Lewis, H. G., Atkinson, P. M. and Nixon, M. S. Super-resolution land cover pattern prediction using a Hopfield neural network. Remote Sens. Environ., 2002, 79, 1–14.
   Web of Science ®Google Scholar
• Ranchin, T., Aiazzi, B., Alparone, L., Baronti, S. and Wald, L. Image fusion—the ARSIS concept and some successful implementation schemes. Photogram. Remote Sens., 2003, 58, 4–18.
   Web of Science ®Google Scholar
• Gonzalez-Audicana, M., Saleta, J. L., Catalan, R. G. and Garcia, R. Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Trans. Geosci. Remote Sens., 2004, 46(6), 1291–1299.
   Web of Science ®Google Scholar
• Pohl, C. and Van Genderen, J. L. Multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens., 1998, 19(5), 823–845.
   Web of Science ®Google Scholar
• Richards, J. A. and Jia, X. Segmented principal components transformation for efficient hyperspectral remote sensing image display and classification. IEEE Trans. Geosci. Remote Sens., 1999, 37(1), 538–542.
   Web of Science ®Google Scholar
• Tsagaris, V. and Anastassopoulos, V. Multispectral image fusion based on perceptual attributes. Image Processing for Remote Sensing, Proc. SPIE, Vol. 5238, 2003, 357–367 (SPIE, Bellingham, WA).
   Google Scholar
• Tsagaris, V. and Anastassopoulos, V. Multispectral image fusion for improved RGB representation based on perceptual attributes. Int. J. Remote Sens. to be published.
   Web of Science ®Google Scholar
• Plaziac, N. Image interpolation using neural networks. IEEE Trans. Image Process., 1999, 8(11), 1647–1651.
   PubMed Web of Science ®Google Scholar
• Sekiwa, D. and Taguchi, A. Enlargement of digital images by using multi-neural networks. Electron. Commun. Jpn, 2001, 84, part 3(11), 790–798.
   Web of Science ®Google Scholar
• Elad, M. and Feuer, A. Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Trans. Image Process., 1997, 6(12), 1646–1658.
   PubMed Web of Science ®Google Scholar
• Nguyen, N., Milanfar, P. and Golub, G. Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement. IEEE Trans. Image Process., 2001, 10, 1299–1308.
   PubMed Web of Science ®Google Scholar
• European Space Agency—EnviSat. MERIS Handbook, Issue 1.1, 1 December 2002.
   Google Scholar
• Patterson, D. W. Artificial Neural Networks: Theory and Applications, 1996 (Prentice-Hall, Singapore).
   Google Scholar