Quality assessment of ten fusion techniques applied on Worldview-2

References

• Abdikan S., Sanli F.S., Sunar F., Ehlers M. (2014)—A comparative data-fusion analysis of multi-sensor satellite images. International Journal of Digital Earth, 7 (8): 671–687. doi: http://dx.doi.org/10.1080/17538947.2012.748846.
   Web of Science ®Google Scholar
• Aiazzi B., Alparone L., Baronti S., Garzelli A. (2002)—Context-Driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. IEEE Transactions on Geoscience and Remote Sensing, 40 (10): 2300–2312. doi: http://dx.doi.org/10.1109/TGRS.2002.803623.
   Web of Science ®Google Scholar
• Aiazzi B., Baronti S., Selva M. (2007)—Improving component substitution pansharpening through multivariate regression of MS + Pan data. IEEE Transactionson Geoscience and Remote Sensing, 45 (10): 3230–3239. doi: http://dx.doi.org/10.1109/TGRS.2007.901007.
   Web of Science ®Google Scholar
• Alparone L., Baronti S., Garzelli A., Nencini F. (2004)—A global quality measurement of Pan-sharpened multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 1 (4): 313–317. doi: http://dx.doi.org/10.1109/LGRS.2004.836784.
   Web of Science ®Google Scholar
• Alparone L., Wald L., Chanussot J., Thomas C., Gamba P., Bruce L.M. (2007)—Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest. IEEE Transactions on Geoscience and Remote Sensing, 45 (10): 3012–3021. doi: http://dx.doi.org/10.1109/TGRS.2007.904923.
   Web of Science ®Google Scholar
• Aschbacher J., Lichtenegger, J. (1990)—Complementary nature of SAR and optical data: a case study in the Tropics. Earth Observation Quarterly, 31: 4–8.
   Google Scholar
• Ashraf S.A., Brabyn L., Hicks B.J. (2012)—Image data fusion for the remote sensing of freshwater environments. Applied Geography, 32: 619–628. doi: http://dx.doi.org/10.1016/j.apgeog.2011.07.010.
   Web of Science ®Google Scholar
• Bloom A., Fielding E., Fu X. (1988)—A demonstration of stereophotogrammetry with combined SIR-B and Landsat-TM images. International Journal of Remote Sensing, 9: 1023–1038. doi: http://dx.doi.org/10.1080/01431168808954911.
   Web of Science ®Google Scholar
• Borel C.C., Spencer C.H. (2009)—Novel methods for panchromatic sharpening of multi/hyperspectral image data. Proceedings of IEEE IGARSS 2009, 4: 757–760. doi: http://dx.doi.org/10.1109/IGARSS.2009.5417487.
   Google Scholar
• Carper W.J., Lillesand, T.M., Kiefer R.W. (1990)—The use of Intensity-Hue-Saturation Transform for merging SPOT Panchromaticand multi-spectral image data. Photogrammetric Engineering and Remote Sensing, 56: 459–467.
   Web of Science ®Google Scholar
• Chavez P.S. Jr., Sides S.C., Anderson J.A. (1991)—Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT Panchromatic. Photogrammetric Engineering and Remote Sensing, 57 (3): 295–303.
   Web of Science ®Google Scholar
• Choi M., Rae Y.K., Myeong-Ryong N., Hong O.K. (2005)—Fusion of Multispectral and Panchromatic Satellite Images Using the Curvelet Transform. IEEE Geoscience and Remote Sensing Letters, 2 (2): 136–140. doi: http://dx.doi.org/10.1109/LGRS.2005.845313.
   Web of Science ®Google Scholar
• Crippen R.E. (1989)—A Simple Spatial filtering Routine for the Cosmetic Removal of Scan-Line Noise from Landsat TM P-Tape Imagery. Photogrammetric Engineering and Remote Sensing, 55 (3): 327–331.
   Web of Science ®Google Scholar
• De Bethune S., Muller F., Donnay J.P. (1998)—Fusion of Multispectral and Panchromatic Images by Local Mean and Variance Matching Filtering Techniques, In: Ranchin T., Wald L. (Eds.), 2^nd international conference on fusion of earth data: Merging point measurements, raster maps and remotely sensed images, pp. 1–6.
   Google Scholar
• Del Carmen V., Inamura M. (2001)—Improvement of remotely sensed low spectral resolution images by back-propagated neural networks using data fusion techniques. International Journal of Remote Sensing, 22: 629–642. doi: http://dx.doi.org/10.1080/01431160050505892.
   Google Scholar
• DigitalGlobe (2010)—White Paper: The Benefits of the 8 Spectral Bands of WorldView-2, p. 12.
   Google Scholar
• Duguay G., Holder G., Howarth P., Ledrew E. (1987)—Integrating remotely sensed data from different sensors for change detection. Proceedings of the International Geoscience and Remote Sensing Symposium, New York, pp. 567.
   Google Scholar
• Ehlers M. (2004)—Spectral characteristics preserving imagefusion based on Fourier domain filtering. Proceedings of SPIE5574, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology, IV (1): 1–13. doi: http://dx.doi.org/10.1117/12.565160.
   Google Scholar
• Ehlers M., Klonus S., Astrand P. J. (2008)—Quality assessment for multi-sensor multi-date image fusion. Proceedings of XXI^th International Congress ISPRS, Beijing, pp. 499–506.
   Google Scholar
• Ehlers M., Klonus S., Johan Ã., Strand P.R., Rosso P. (2010)—Multi-sensor image fusion forpansharpening in remote sensing. International Journal of Image and Data Fusion, 1 (1): 25–45. doi: http://dx.doi.org/10.1080/19479830903561985.
   Google Scholar
• Envi (2008)—Envi User's Guide Version 4.5, p. 1152.
   Google Scholar
• Erdas 2008)—Erdas Field Guide, p. 770.
   Google Scholar
• Fanelli A., Leo A., Ferri M. (2001)—Remote Sensing Images Data Fusion: A Wavelet Transform Approach for Urban Analysis. Proceedings of the IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Rome, pp. 112–116. doi: http://dx.doi.org/10.1109/DFUA.2001.985737.
   Google Scholar
• Gangkofner U.G., Pradhan P.S., Holcomb D.W. (2008)—Optimizing the high-pass filter addition technique for image fusion. Photogrammetric Engineering and Remote Sensing, 74 (9): 1107–1118. doi: http://dx.doi.org/10.14358/PERS.74.9.1107.
   Web of Science ®Google Scholar
• Gillespie A.R., Kahle A.B., Walker R.E. (1987)—Color enhancement of highly correlated images-ll. Channel ratio and “chromaticity” transformation techniques. Remote Sensing of Environment, 22: 343–365. doi: http://dx.doi.org/10.1016/0034-4257(87)90088-5.
   Web of Science ®Google Scholar
• Gonzalez-Audicana M., Otazu X., Fors O., Seco A. (2005)—Comparison between Mallat's and the ‘a’ trous' discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. International Journal of Remote Sensing, 26 (3): 595–614. doi: http://dx.doi.org/10.1080/01431160512331314056.
   Web of Science ®Google Scholar
• Han S.S., Li H.T., Gu H.Y. (2008)—The study on image fusion for high spatial resolution remote sensing images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII (B7): 1159–1164.
   Google Scholar
• Han Z., Tang X., Gao X., Hu F. (2013)—Image Fusion and image quality assesment of Fused images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W1, 3rd ISPRS IWIDF, pp. 33–36. doi: http://dx.doi.org/10.5194/isprsarchives-XL-7-W1-33-2013.
   Google Scholar
• Haydn R., Dalke G.W., Henkel J., Bare J.E. (1982)—Applications of the IHScolor transform to the processing of multisensor data and image enhancement. Proceedings of the International Symposium of Remote Sensing of Arid and Semi-arid Lands, Cairo, pp. 599–616.
   Google Scholar
• Hill J., Diemer C., Stöver O., Udelhoven Th. (1999)—A local correlation approach for the fusion of remote sensing data with different spatial resolutions in forestry applications. International Archives of Photogrammetry and Remote Sensing Valladolid, Spain, 32 (Part 7-4-3 W6).
   Google Scholar
• Jawak S.D., Alvarinho J.L. (2013)—A spectral index ratio-based Antarctic land-cover mapping using hyperspatial 8-band WorldView-2 imagery. Polar Science, 7: 18–38. doi: http://dx.doi.org/10.1016/j.polar.2012.12.002.
   Google Scholar
• Joly G. (1986)—Traitements des fichiers images. Collection Télédetection satellitaire, N. 3, Paradigme, Caen, p. 137.
   Google Scholar
• Johnson B.A., Tateishi R., Thanh Hoan N. (2013). A hybrid pansharpening approach and multiscale object based image analysis for mapping diseased pine and oak trees. International Journal of Remote Sensing, 34 (20): 6969–6982. doi: http://dx.doi.org/10.1080/01431161.2013.810825.
   Web of Science ®Google Scholar
• Kalpoma K.A., Kudoh J. (2007)—Image fusion processing for IKONOS 1-m color imagery. IEEE Transactions on Geoscience and Remote Sensing, 45 (10): 3075–3086. doi: http://dx.doi.org/10.1109/TGRS.2007.897692.
   Web of Science ®Google Scholar
• Karathanassi V., Kolokousis P., Ioannidou S. (2007)—A comparison study on fusion methods using evaluation indicators. International Journal of Remote Sensing, 28 (10): 2309–2341. doi: http://dx.doi.org/10.1080/01431160600606890.
   Web of Science ®Google Scholar
• King R.L., Wang J. (2001)—A wavelet based algorithm for pan sharpening Landsat 7 imagery. Proceedings of IEEE Geoscience and Remote Sensing Symposium, 2: 849–851. doi: http://dx.doi.org/10.1109/IGARSS.2001.976657.
   Google Scholar
• Klonus S., Ehlers M. (2007)—Image fusion using the Ehlers spectral characteristics preservation algorithm. GIScience and Remote Sensing, 44 (2): 93–116. doi: http://dx.doi.org/10.2747/1548-1603.44.2.93.
   Web of Science ®Google Scholar
• Laben C.A., Bernard V., Brower W. (2000)—Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. United States Patent Application 6011875.
   Google Scholar
• Laporterie-Dejean F., de Boissezon H., Flouzat G., Lefevre-Fonollosa M. (2005)—Thematic and statistical evaluations of five panchromatic/multispectral fusion methods on simulatedpleiades-hr images. Information Fusion, 6 (3): 193–212. doi: http://dx.doi.org/10.1016/j.inffus.2004.06.006.
   Google Scholar
• Leckie D.G. (1990)—Synergism of SAR and visible/infrared data for forest type discrimination. Photogrammetric Engineering and Remote Sensing, 56: 1237–1246.
   Web of Science ®Google Scholar
• Lemeshewsky G.P. (1999)—Multispectral multisensor image fusion using wavelet transforms. Proceedings of SPIE 3716: 214–222. doi: http://dx.doi.org/10.1117/12.354709.
   Google Scholar
• Lemeshewsky G.P. (2002)—Multispectral Image sharpening Using a Shift-Invariant Wavelet Transform and Adaptive Processing of Multiresolution Edges. Proceedings of SPIE, 4736: 189–200. doi: http://dx.doi.org/10.1117/12.477580.
   Google Scholar
• Li H., Manjunath B.S., Mitra S.K. (1995)—Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57: 234–245. doi: http://dx.doi.org/10.1006/gmip.1995.1022.
   Google Scholar
• Li J. (2000)—Spatial quality evaluation of fusion of different resolution images. International Archives of Photogrammetry and Remote Sensing, 33: 339–346.
   Google Scholar
• Lillo-Saavedra M., Gonzalo C., Arquero A., Martinez E. (2005)—Fusion of multispectral and panchromatic satellite sensor imagery based on tailored filtering in the Fourier domain. International Journal of Remote Sensing, 26 (6): 1263–1268. doi: http://dx.doi.org/10.1080/01431160412331330239.
   Web of Science ®Google Scholar
• Ling Y., Ehlers M., Usery L., Madden M. (2007)—FFT-enhancedIHS transform methodfor fusing high resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 61: 381–392. doi: http://dx.doi.org/10.1016/jisprsjprs.2006.11.002.
   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: http://dx.doi.org/10.1080/014311600750037499.
   Web of Science ®Google Scholar
• Nencini F., Garzelli A., Baronti S., Alparone L. (2007)—Remote sensing image fusion using the curvelet transform. Information Fusion, 8: 143–156. doi: http://dx.doi.org/10.1016/j.inffus.2006.02.001.
   Web of Science ®Google Scholar
• Nikolakopoulos K.G. (2008)—Comparison of Nine Fusion Techniques for Very High Resolution Optical Data. Photogrammetric Engineering and Remote Sensing, 74 (5): 647–659. doi: http://dx.doi.org/10.14358/PERS.74.5.647.
   Web of Science ®Google Scholar
• Nikolakopoulos K.G., Vaiopoulos A.D., Tsombos P.I. (2010)—Ameliorating the spatial resolution of Geoeye data. Proceedings of SPIE, 7831 (04): 1–11. doi: http://dx.doi.org/10.1117/12.864442.
   Google Scholar
• Otazu X., González-Audícana M., Fors O., Núnez J. (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: http://dx.doi.org/10.1109/TGRS.2005.856106.
   Web of Science ®Google Scholar
• Piella G., Heijmans H. (2003)—A new quality metric for image fusion. Proceedings of IEEE International Conference on Image Processing, 3: 173–176. doi: http://dx.doi.org/10.1109/ICIP.2003.1247209.
   Google Scholar
• Pradhan P.S., King R.L., Younan N.H., Holcomb D.W. (2006)—Estimation of the number of decomposition levels for a wavelet-based multiresolution multisensor image fusion. IEEE Transactions on Geoscience and Remote Sensing, 44 (12): 3674–3686. doi: http://dx.doi.org/10.1109/TGRS.2006.881758.
   Web of Science ®Google Scholar
• Price J.C. (1987)—Combining panchromatic and multispectral imageryfrom dual resolution satellite instruments. Remote Sensing of Environment, 21: 119–128. doi: http://dx.doi.org/10.1016/0034-4257(87)90049-6.
   Web of Science ®Google Scholar
• Ranchin T., Aiazzi B., Alparone L., Baronti S., Wald L. (2003)—Image fusion the ARSIS concept and some successful implementation schemes. ISPRS Journal of Photogrammetry and Remote Sensing, 58 (1–2): 4–18. doi: http://dx.doi.org/10.1016/S0924-2716(03)00013-3.
   Web of Science ®Google Scholar
• Sarp G. (2014)—Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul. European Journal of Remote Sensing, 47:19–28. doi: http://dx.doi.org/10.5721/EuJRS20144702.
   Web of Science ®Google Scholar
• Schistad-Solberg A.H., Jain A.K., Taxt T. (1994)—Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images. IEEE Transactions on Geoscience and Remote Sensing, 32: 768–778. doi: http://dx.doi.org/10.1109/36.298006.
   Web of Science ®Google Scholar
• Shahdoosti H.R., Ghassemian H. (2015)—Fusion of MS and PAN Images Preserving Spectral Quality, IEEE Geoscience and Remote Sensing Letters, 12 (3): 611–615. doi: http://dx.doi.org/10.1109/LGRS.2014.2353135.
   Web of Science ®Google Scholar
• Shah V.P., Younan N.H., King R.L. (2008)—Joint spectral and spatial quality evaluation of pansharpening methods. Journal of Applied Remote Sensing, 2 (1): 023531. doi: http://dx.doi.org/10.1117/1.2977460.
   Google Scholar
• Shannon C.E. (1948)—A Mathematical Theory of Communication. Bell System Technical Journal, 27: 379–423 and 623–656, July and October 1948. doi: http://dx.doi.org/10.1002/j.1538-7305.1948.tb01338.x.
   Web of Science ®Google Scholar
• Schowengerdt R.A. (1980)—Reconstruction of Multispatial, Multispectral Image Data Using Spatial Frequency Content. Photogrammetric Engineering and Remote Sensing, 46 (10): 1325–1334.
   Web of Science ®Google Scholar
• Siddiqui Y. (2003)—The modified IHS method for fusing satellite imagery. ASPRS 2003 annual conference. Anchorage, Alaska: American Society for Photogrammetry and Remote Sensing (CD-ROM).
   Google Scholar
• Skianis G.A., Nikolakopoulos K. (2009)—The entropy as a measure of the performance of the NDVI vegetation index-a pilot study with an ALOS digital image. Proceedings of ALOS PI 2008 Symposium, Island of Rhodes, ESA SP-664, Unpaginated CDRom.
   Google Scholar
• Strobl D., Raggam J., Buchroithner M.F. (1990)—Terrain correction geocoding of a multisensor image data set. Proceedings of the 10^th EARSEL Symposium, Toulouse, France, pp. 98–107.
   Google Scholar
• Suits G., Malila W., Weller T. (1988)—Procedures for using signals from one sensor as substitutes for signals of another. Remote Sensing of Environment, 25: 395–408. doi: http://dx.doi.org/10.1016/0034-4257(88)90111-3.
   Web of Science ®Google Scholar
• Thomas C., Wald L. (2006)—Comparing distances for quality assessment of fused products. Proceedings of 26^th EARSeL Annual Symposium New Development and Challenges in Remote Sensing, Warsaw, Poland, Bochenek Z.(Ed.), Rotterdam, The Netherlands: Balkema, 2007, pp. 101–111.
   Google Scholar
• Vaiopoulos D., Nikolakopoulos K., Skianis G.A. (2001)—A comparative study of resolution merge techniques and their efficiency in processing images of urban areas. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Rome, pp. 270–274. doi: http://dx.doi.org/10.1109/DFUA.2001.985894.
   Google Scholar
• Vijayaraj V., Nicolas H.Y., Charles G.O.H. (2006)—Quantitative analysis of pansharpened images. Optical Engineering, 45 (4): 046202. doi: http://dx.doi.org/10.1117/L2195987.
   Web of Science ®Google Scholar
• Vrabel J. (1996)—Multispectral imagery band sharpening study. Photogrammetric Engineering and Remote Sensing, 62 (9): 1075–1083.
   Web of Science ®Google Scholar
• Vrabel J., Doraiswamy P., McMurtrey J., Stern A. (2002)—Demonstration of the Accuracy of Improved Resolution Hyperspectral Imagery. Proceedings of SPIE, 4725: 556–567. doi: http://dx.doi.org/10.1117/12.478790.
   Google Scholar
• Wald L., Ranchin Th., Mangolini M. (1997)—Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering and Remote Sensing, 63 (6): 691–699.
   Web of Science ®Google Scholar
• Wald L. (2000)—Quality of high resolution synthesised images: is there a simple criterion? In: Ranchin T., Wald L. (Eds.), Fusion of earth data: Merging point measurements, raster maps and remotely sensed images, pp. 166, Nice, France, Sophia Antipolis, France: SEE/URISCA.
   Google Scholar
• Wald L. (2002)—Data Fusion: Definitions and Architectures-Fusion of Images of Different Spatial Resolutions. Les Presses de l'Ecole des Mines, Paris.
   Google Scholar
• Wang Z., Bovik A.C. (2002)—A universal image quality index. IEEE Signal Processing Letters, 9 (3): 81–84. doi: http://dx.doi.org/10.1109/97.995823.
   Web of Science ®Google Scholar
• Wang Z., Bovik A.C., Sheik H., Simoncelli E. (2004)—Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13 (4): 600–612. doi: http://dx.doi.org/10.1109/TIP.2003.819861.
   PubMed Web of Science ®Google Scholar
• Wang Z., Ziou D., Armenakis C., Li D., Li Q. (2005)—A comparative analysis of image fusion methods. IEEE Transactions on Geoscience and Remote Sensing, 43 (6): 1391–1402. doi: http://dx.doi.org/10.1109/TGRS.2005.846874.
   Web of Science ®Google Scholar
• Wang Z., Zhao Z. (2008)—Expanded Q4 Quality Assessment for Pan-Sharpened MultiSpectral Image. Proceedings of SPIE 7146, 71460V1-8. doi: http://dx.doi.org/10.1117/12.813122.
   Google Scholar
• Wang Z., Liu S., You S., Huang X. (2010)—Simulation of Low-Resolution Panchromatic Images by Multivariate Linear Regression for Pan-Sharpening IKONOS Imageries. IEEE Geoscience and Remote Sensing Letters, 7 (3): 515–519. doi: http://dx.doi.org/10.1109/LGRS.2010.2040706.
   Web of Science ®Google Scholar
• Welch R., Ehlers M. (1987)—Merging multiresolution SPOTHRV and Landsat TM data. Photogrammetric Engineering and Remote Sensing, 53 (3): 301–303.
   Web of Science ®Google Scholar
• Wiener N. (1948)—Cybernetics or Control and Communication in the Animal and the Machine. Hermann and Cie (Eds), Paris.
   Google Scholar
• Witharana C., Civco D.L., Meyer T.H. (2013)—Evaluation of pansharpening algorithms in support of earth observation based rapid-mapping workflows. Applied Geography, 37: 63–87. doi: http://dx.doi.org/10.1016/j.apgeog.2012.10.008.
   Web of Science ®Google Scholar
• Yang B., Kim M., Madden M. (2012)—Assessing optimal image fusion methods for very high spatial resolution satellite images to support coastal monitoring. GIScience and Remote Sensing, 49 (5): 687–710. doi: http://dx.doi.org/10.2747/1548-1603.49.5.687.
   Web of Science ®Google Scholar
• Yakhdani M.F., Azizi A. (2010)—Quality assessment of image fusion techniques for multisensor high resolution satellite images (case study: IRS-p5 and lRS-p6 satellite images). In:. Székely W.W., and B (Eds.), ISPRS TC VII symposium—100 years ISPRS (pp. Part7B). Vienna, Austria: IAPRS, pp. 204–209.
   Google Scholar
• Yuhendra I.A., Tetuko J. Sumantyo S., Kuze H. (2012)—Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data. International Journal of Applied Earth Observation and Geoinformation, 18: 165–175. doi: http://dx.doi.org/10.1016/j.jag.2012.01.013.
   Web of Science ®Google Scholar
• Zeng Y., Huang W., Liu M., Zhang H., Zou B. (2010)—Fusion of satellite images in urban area: Assessing the quality of resulting images. 18^th International Conference on Geoinformatics, 1–4. doi: http://dx.doi.org/10.1109/GEOINFORMATICS.2010.5568105.
   Google Scholar
• Zhang Y. (1999)—A new merging method and its spectral and spatial effects. International Journal of Remote Sensing, 20: 2003–2014. doi: http://dx.doi.org/10.1080/014311699212317.
   Web of Science ®Google Scholar
• Zhang Y. (2002)—A new automatic approach for effectively fusing Landsat-and as well as IKONOS images. IEEE Transactions on Geoscience and Remote Sensing, 4: 2429–2431. doi: http://dx.doi.org/10.1109/IGARSS.2002.1026567.
   Google Scholar
• Zhang Y. (2008)—Methods for image fusion quality assessment. A review, comparison and analysis. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XXXVII: 1101–1109.
   Google Scholar
• Zhang Y., Mishra R.K. (2012)—A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion. IEEE Geoscience and Remote Sensing Symposium, pp. 182–185. doi: http://dx.doi.org/10.1109/IGARSS.2012.6351607.
   Google Scholar
• Zhou J., Civco D.L., Silander J.A. (1998)—A wavelet transform method to merge Landsat TM and SPOT panchromatic data. International Journal of Remote Sensing, 19: 743–757. doi: http://dx.doi.org/10.1080/014311698215973.
   Web of Science ®Google Scholar
• Zhang Q., Cao Z., Hu Z., Jia Y., Wu X. (2015)—Joint Image Registration and Fusion for Panchromatic and Multispectral Images. IEEE Geoscience and Remote Sensing Letters, 12 (3): 467–471. doi: http://dx.doi.org/10.1109/LGRS.2014.2346398.
   Google Scholar