Recent advances in pansharpening and key problems in applications

References

• Abdi, H. and Williams, L.J., 2010. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433–459. doi:10.1002/wics.101.
   Google Scholar
• Aiazzi, B., Baronti, S., and Selva, M., 2007. Improving component substitution pansharpening through multivariate regression of MS+Pan data. IEEE Transactions on Geoscience and Remote Sensing, 45 (10), 3230–3239. doi:10.1109/TGRS.2007.901007.
   Web of Science ®Google Scholar
• Aiazzi, B., et al., 2002a. Context-driven fusion of high spatial and spectral resolution images based on oversampled multi-resolution analysis. IEEE Transactions on Geoscience and Remote Sensing, 40 (10), 2300–2312. doi:10.1109/TGRS.2002.803623.
   Web of Science ®Google Scholar
• Aiazzi, B., et al., 2002b. Generalised Laplacian pyramid-based fusion of MS+P image data with spectral distortion minimization [online]. Available from: http://www.isprs.org/proceedings/XXXIV/part3/papers/paper083.pdf [Accessed 24 June 2011].
   Google Scholar
• Aiazzi, B., et al., 2003a. Context modelling for joint spectral and radiometric distortion minimization in pyramid-based fusion of MS and P image data. In: S.B. Serpico, ed. Image and signal processing for remote sensing VIII. Proc SPIE, 4885. 46–57.
   Google Scholar
• Aiazzi, B., et al., 2003b. An MTF-based spectral distortion minimizing model for pan-sharpening of very high resolution multispectral images of urban areas. 2nd GRSS/ISPRS joint workshop on remote sensing and data fusion over urban areas, 90–94.
   Google Scholar
• Aiazzi, B., et al., 2010. A theoretical evaluation of aliasing and misregistration effects on pansharpening methods. In Proceedings of SPIE, Image and Signal Processing for Remote Sensing XVI, 7830 (1), 1–9.
   Google Scholar
• Amolins, K., Zhang, Y., and Dare, P., 2007. Wavelet based image fusion techniques: an introduction, review and comparison. ISPRS Journal of Photogrammetry & Remote Sensing, 62, 249–263. doi:10.1016/j.isprsjprs.2007.05.009.
   Web of Science ®Google Scholar
• Amrol, I., et al., 2011. A survey of classical methods and new trends in pansharpening of multispectral images. EURASIP Journal on Advances in Signal Processing, 2011 (79), 1–22.
   Google Scholar
• Arfken, G., 1985. Gram–Schmidt orthogonalization. Mathematical methods for physicists. 3rd ed. Orlando, FL: Academic Press, 516–520.
   Google Scholar
• Baltsavias, E., Pateraki, M., and Zhang, L., 2001. Radiometric and geometric evaluation of Ikonos geo images and their use for 3D building modelling. Joint ISPRS workshop ‘high resolution mapping from space 2001’, 19–21 September 2001, Hannover, Germany: ISPRS, 1–21.
   Google Scholar
• Blanc, P., Wald, L., and Ranchin, T., 1998. Importance and effect of coregistration quality in an example of ‘pixel to pixel’ fusion process. In proceedings of 2nd conf. fusion earth data: merging point meas., raster maps remotely sensed images, 28–30 January 1998. Sophia Antipolis, France: N/P, 67–73.
   Google Scholar
• Brauers, J. and Aach, T., 2009. Ghosting reflection compensation for multispectral high dynamic range imaging. In IS&T 17th color imaging conference, 9–13 November 2009. Albuquerque, NM: Wiley Subscription Services, 170–174.
   Google Scholar
• Candès, E., Romberg, J., and Tao, T., 2006. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59 (8), 1207–1223. doi:10.1002/cpa.20124.
   Web of Science ®Google Scholar
• Carper, J.W., Lillesand, T.M., and Kiefer, R.W., 1990. The use of intensity–hue–saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering & Remote Sensing, 56 (4), 459–467.
   Web of Science ®Google Scholar
• Chang, X., et al., 2010. Multicontourlet-based adaptive fusion of infrared and visible remote sensing images. IEEE Geoscience and Remote Sensing Letters, 7 (3), 549–553. doi:10.1109/LGRS.2010.2041323.
   Web of Science ®Google Scholar
• Chavez, P., Sides, S., and Anderson, J., 1991. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogrammetric Engineering & Remote Sensing, 57 (3), 295–303.
   Web of Science ®Google Scholar
• Chen, S., et al., 2008. Fusing remote sensing images using à trous wavelet transform and empirical mode decomposition. Pattern Recognition Letters, 29 (3), 330–342. doi:10.1016/j.patrec.2007.10.013.
   Web of Science ®Google Scholar
• Chien, C.L. and Tsai, W.H., 2013. Image fusion with no gamut problem by improved nonlinear IHS transform for remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 99, 1–13.
   Google Scholar
• Choi, J., Yu, K., and Kim, Y., 2011. A new adaptive component-substitution-based satellite image fusion by using partial replacement. IEEE Transactions on Geoscience and Remote Sensing, 49 (1), 295–309. doi:10.1109/TGRS.2010.2051674.
   Web of Science ®Google Scholar
• Choi, M., 2006. A new intensity–hue–saturation fusion approach to image fusion with a trade-off parameter. IEEE Transactions on Geoscience and Remote Sensing, 44 (6), 1672–1682. doi:10.1109/TGRS.2006.869923.
   Web of Science ®Google Scholar
• Choi, M., et al., 2005. Fusion of multispectral and panchromatic satellite images using the curvelet transform. IEEE Geoscience and Remote Sensing Letters, 2 (2), 136–140. doi:10.1109/LGRS.2005.845313.
   Web of Science ®Google Scholar
• Cliché, G., Bonn, F., and Teillet, P., 1985. Integration of the SPOT pan channel into its multispectral mode for image sharpness enhancement. Photogrammetric Engineering & Remote Sensing, 51, 311–316.
   Web of Science ®Google Scholar
• Donoho, D.L., 2006. Compressed sensing. IEEE Transactions on Information Theory, 52 (4), 1289–1306. doi:10.1109/TIT.2006.871582.
   Web of Science ®Google Scholar
• Ehlers, M., Klonus, S., and Astrand, P., 2008. Quality assessment for multi-sensor multidate image fusion. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII (Part B4), 499–505.
   Google Scholar
• Ehlers, M., et al., 2010. Multi-sensor image fusion for pansharpening in remote sensing. International Journal of Image and Data Fusion, 1 (1), 25–45. doi:10.1080/19479830903561985.
   Google Scholar
• ENVI, 2012. ENVI ver. 4.8 software help document. Boulder, CO: ITT – Visual Information Solutions.
   Google Scholar
• ERDAS, 2010. ERDAS Imagine software help document. Norcross, GA: ERDAS Inc.
   Google Scholar
• ESRI ArcGIS Help 10.1, 2013. Fundamentals of panchromatic sharpening [online]. Available from: http://resources.arcgis.com/en/help/main/10.1/index.html#//009t000000mw000000 [Accessed 3 January 2013].
   Google Scholar
• Filippidis, A., Jain, L.C., and Martin, N., 2000. Multisensor data fusion for surface land-mine detection. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 30 (1), 145–150. doi:10.1109/5326.827491.
   Web of Science ®Google Scholar
• Gangkofner, U.G., Pradhan, P.S., and Holcomb, D.W. 2008. Optimizing the high-pass filter addition technique for image fusion. Photogrammetric Engineering & Remote Sensing, 74 (9), 1107–1118. doi:10.14358/PERS.74.9.1107.
   Web of Science ®Google Scholar
• Gillespie, A.R., Kahle, A.B., and Walker, R.E., 1987. Color enhancement of highly correlated images. II. channel ratio and ‘chromaticity’ transformation techniques. Remote Sensing of Environment, 22, 343–365. doi:10.1016/0034-4257(87)90088-5.
   Web of Science ®Google Scholar
• Gonzalez-Audicana, M., et al., 2004. Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 42 (6), 1291–1299. doi:10.1109/TGRS.2004.825593.
   Web of Science ®Google Scholar
• Guenther, B., et al., 1996. MODIS calibration: a brief review of the strategy for the at launch calibration approach. Journal of Atmospheric and Oceanic Technology, 13, 274–285. doi:10.1175/1520-0426(1996)013<0274:MCABRO>2.0.CO;2.
   Web of Science ®Google Scholar
• Huang, P.S. and Tu, T.-M., 2003. A target fusion-based approach for classifying high spatial resolution imagery. In 2003 IEEE workshop on advances in techniques for analysis of remotely sensed data, 27–28 October, Greenbelt, MD, 175–181.
   Google Scholar
• Jiang, C., et al., 2012. A practical compressed sensing-based pan-sharpening method. IEEE Geoscience and Remote Sensing Letters, 9 (4), 629–633. doi:10.1109/LGRS.2011.2177063.
   Web of Science ®Google Scholar
• Jing, L. and Cheng, Q., 2011. An image fusion method for misaligned panchromatic and multispectral data. International Journal of Remote Sensing, 32 (4), 1125–1137. doi:10.1080/01431160903527405.
   Web of Science ®Google Scholar
• King, R. and Wang, J., 2001. A Wavelet based algorithm for pan sharpening Landsat 7 imagery. IEEE international geoscience and remote sensing symposium, 9–13 July 2001, Sydney. Sydney: IEEE, 849–851.
   Google Scholar
• Laben, C.A. and Brower, B.V., 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. Technical Report, US Patent No. 6011875. Eastman Kodak Company.
   Google Scholar
• Li, S. and Yang, B., 2011. A new pan-sharpening method using a compressed sensing technique. IEEE Transactions on Geoscience and Remote Sensing, 49 (2), 738–746. doi:10.1109/TGRS.2010.2067219.
   Web of Science ®Google Scholar
• Liu, J.G., 2000. Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21 (18), 3461–3472. doi:10.1080/014311600750037499.
   Web of Science ®Google Scholar
• Mahyari, A.G. and Yazdi, M., 2011. Panchromatic and multispectral image fusion based on maximization of both spectral and spatial similarities. IEEE Transactions on Geoscience and Remote Sensing, 49 (6), 1976–1985. doi:10.1109/TGRS.2010.2103944.
   Web of Science ®Google Scholar
• Massip, P., Blanc, P., and Wald, L., 2012. A method to better account for modulation transfer functions in arsis-based pansharpening methods. IEEE Transactions on Geoscience and Remote Sensing, 50 (3), 800–808. doi:10.1109/TGRS.2011.2162244.
   Google Scholar
• Moigne, L., et al., 2000. Geo-registration of Landsat data by robust matching of wavelet features. IEEE International Proceedings Geoscience and Remote Sensing Symposium, 4, 1610–1612.
   Google Scholar
• Nencini, F., et al., 2007. Remote sensing image fusion using the curvelet transform. Information Fusion, 8 (2), 143–156. doi:10.1016/j.inffus.2006.02.001.
   Web of Science ®Google Scholar
• Nunez, J., et al., 1999. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 37 (3), 1204–1211. doi:10.1109/36.763274.
   Web of Science ®Google Scholar
• Otazu, X., et al., 2005. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing, 43 (10), 2376–2385. doi:10.1109/TGRS.2005.856106.
   Web of Science ®Google Scholar
• PCI Geomatica, 2013. Pan-sharpening technical specification [online]. Available from: http://www.pcigeomatics.com/pdf/PanSharp.pdf [Accessed 25 February 2014].
   Google Scholar
• Pohl, C. and van Genderen, J.L., 1998. Review article multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823–854. doi:10.1080/014311698215748.
   Web of Science ®Google Scholar
• Rahmani, S., et al., 2010. An adaptive IHS pan-sharpening method. IEEE Geoscience and Remote Sensing Letters, 7 (4), 746–750. doi:10.1109/LGRS.2010.2046715.
   Web of Science ®Google Scholar
• Ranchin, T., et al., 2003. Image fusion-the ARSIS concept and some successful implementation schemes. Photogrammetric Engineering & Remote Sensing, 58 (1–2), 4–18.
   Web of Science ®Google Scholar
• SceneSharp, 2014. Fuze Go-GIS imaging software [online]. Available from: http://www.scenesharp.com/innovation/fuze-go [Accessed 25 February 2014].
   Google Scholar
• Schowengerdt, R.A., 1997. Remote sensing: models and methods for image processing. 3rd ed. Orlando, FL: Academic.
   Google Scholar
• Shah, V.P., Younan, N.H., and King, R.L., 2008. An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Transactions on Geoscience and Remote Sensing, 46 (5), 1323–1335. doi:10.1109/TGRS.2008.916211.
   Web of Science ®Google Scholar
• Thomas, C., et al., 2008. Synthesis of multispectral images to high spatial resolution: a critical review of fusion methods based on remote sensing physics. IEEE Transactions on Geoscience and Remote Sensing, 46 (5), 1301–1312. doi:10.1109/TGRS.2007.912448.
   Web of Science ®Google Scholar
• Tsai, V.J.D., 2003. Frequency-based fusion of multiresolution images. IEEE international geoscience and remote sensing symposium, 31 July–4 August 2003, Denver, CO: IEEE, 3665–3667.
   Google Scholar
• Tu, T.-M., et al., 2001. A new look at IHS-like image fusion methods. Information Fusion, 2 (3), 177–186. doi:10.1016/S1566-2535(01)00036-7.
   Google Scholar
• Tu, T.-M., et al., 2004. A fast intensity–hue–saturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geoscience and Remote Sensing Letters, 1 (4), 309–312. doi:10.1109/LGRS.2004.834804.
   Web of Science ®Google Scholar
• Tu, T.M., et al., 2007. Best tradeoff for high-resolution image fusion to preserve spatial details and minimize color distortion. IEEE Geoscience and Remote Sensing Letters, 4 (2), 302–306. doi:10.1109/LGRS.2007.894143.
   Web of Science ®Google Scholar
• Vrabel, J., 2000. Multi-spectral imagery advanced band sharpening study. Photogrammetric Engineering & Remote Sensing, 66 (1), 73–79.
   Web of Science ®Google Scholar
• Vrabel, J., et al., 2002. Demonstration of the accuracy of improved-resolution hyperspectral imagery. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, 4725, 556–567. doi:10.1117/12.478790.
   Google Scholar
• Wald, L., 2002. Data fusion: definitions and architectures fusion of images of different spatial resolutions. Paris: Presses de l’Ecole, Ecole des Mines de Paris.
   Google Scholar
• Wang, Q., et al., 2011. A new technique for multispectral and panchromatic image fusion. Procedia Engineering, 24, 182–186. doi:10.1016/j.proeng.2011.11.2623.
   Google Scholar
• Welch, R. and Ehlers, M., 1987. Merging multiresolution SPOT HRV and Landsat TM data. Photogrammetric Engineering & Remote Sensing, 53 (3), 301–303.
   Web of Science ®Google Scholar
• Yang, S., Wang, M., and Jiao, L., 2012. Fusion of multispectral and panchromatic images based on support value transform and adaptive principal component analysis. Information Fusion, 13 (3), 177–184. doi:10.1016/j.inffus.2010.09.003.
   Web of Science ®Google Scholar
• Yang, S., et al., 2010. Image fusion based on a new contourlet packet. Information Fusion, 11 (2), 78–84. doi:10.1016/j.inffus.2009.05.001.
   Web of Science ®Google Scholar
• Zhang, J., 2010. Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1 (1), 5–24. doi:10.1080/19479830903561035.
   Google Scholar
• Zhang, Y., 1999. A new merging method and its spectral and spatial effects. International Journal of Remote Sensing, 20 (10), 2003–2014. doi:10.1080/014311699212317.
   Web of Science ®Google Scholar
• Zhang, Y., 2004. Understanding image fusion. Photogrammetric Engineering & Remote Sensing, 70 (6), 657–661.
   Web of Science ®Google Scholar
• Zhang, Y., 2008. Methods for image fusion quality assessment – a review, comparison and analysis. The XXI ISPRS Congress, Beijing. Published in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XXXVII. Part B7, 1101–1109.
   Google Scholar
• Zhang, Y. and Hong, G., 2005. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images. Information Fusion, 6 (3), 225–234. doi:10.1016/j.inffus.2004.06.009.
   Google Scholar
• Zhang, Y. and Mishra, R., 2013. From UNB PanSharp to PCI PanSharp, DG PanSharp and now ENVI Fuze Go – the success behind the pan-sharpening algorithm. International Journal of Image and Data Fusion. doi:10.1080/19479832.2013.848475.
   Google Scholar
• Zhou, J., Civco, D.L., and Silander, J.A., 1998. A wavelet transform method to merge Landsat TM and SPOT panchromatic data. International Journal of Remote Sensing, 19 (4), 743–757. doi:10.1080/014311698215973.
   Web of Science ®Google Scholar
• Zhu, J., et al., 2004. Fusion of high-resolution remote sensing images based on à trous wavelet algorithm. IEEE international geoscience and remote sensing symposium, 20–24 September 2004. Anchorage, AK: IEEE, 352–355.
   Google Scholar
• Zhu, X.X. and Bamler, R., 2013. A sparse image fusion algorithm with application to pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing, 51 (5), 2827–2836. doi:10.1109/TGRS.2012.2213604.
   Web of Science ®Google Scholar