Multi-sensor Satellite Pan-sharpening based on IHS and Window Pseudo Wigner Distribution Integrated Approach: Application to WorldView-2 Imagery

References

• Aiazzi, B., et al., 2002. Context-driven fusion of high spatial and spectral resolution images based on oversampled multi-resolution analysis. IEEE Transactions on Geo-science and Remote Sensing, 40, 2300–2312. doi:10.1109/TGRS.2002.803623
   Web of Science ®Google Scholar
• ALEjaily, A.M., El Rube, I.A., and Mangoud, M.A., 2008. Fusion of remote sensing images using contourlet transform. Springer Science, 213–218.
   Google Scholar
• Amolins, K., Zhang, Y., and Dare, P., 2007. Wavelet based image fusion techniques – an introduction, review and comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 249–263. doi:10.1016/j.isprsjprs.2007.05.009
   Web of Science ®Google Scholar
• Bamberger, R.H. and Smith, M.J.T., 1992. A filter bank for the directional decomposition of images: theory and design. IEEE Transactions on Signal Processing, 40, 882–893. doi:10.1109/78.127960
   Web of Science ®Google Scholar
• Beaulieu, M., Foucher, S., and Gagnon, L., 2003. Multi-spectral image resolution refinement using stationary wavelet transform. In: Proceedings of the International Geo-science and Remote Sensing Symposium (IGARSS ’03), 4032–4033.
   Google Scholar
• Carper, W.J., 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 and Remote Sensing, 56, 459–467.
   Web of Science ®Google Scholar
• Chavez, P., Berlin, G.L., and Sowers, L.B., 1982. Statistical methods for selecting Landsat MSS ratios. Journal of Applied Photographic Engineering, 8, 23–30.
   Google Scholar
• Chavez, P.S., Sides, S.C., and Anderson, J.A., 1991. Comparison of three different methods to merge multi-resolution and multispectral data Land sat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing, 57, 295–303.
   Web of Science ®Google Scholar
• Cheng, S., Yang, Y., and Li, Y., 2007. Image fusion based on improved IHS and curvelet transform integrated method. In: Proc. SPIE 6752, Geo-informatics 2007: Remotely Sensed Data and Information, 67520S. doi:10.1117/12.760462
   Google Scholar
• Chibani, Y. and Houacine, A., 2002. The joint use of IHS transform and redundant wavelet decomposition for fusing multispectral and panchromatic images. International Journal of Remote Sensing, 23 (18), 3821–3833. doi:10.1080/01431160110107626
   Web of Science ®Google Scholar
• Choi, M., et al., 2005. Fusion of multispectral and panchromatic satellite images using the curvelet transform. IEEE Geo-science Remote Sensing Letters, 2 (2), 136–140. doi:10.1109/LGRS.2005.845313
   Web of Science ®Google Scholar
• Claasen, T.A.C.M. and Mecklenbrauker, W.F.G., 1980. The Wigner distribution – a tool for time–frequency analysis. Parts I–III. Philips Journal Resen, 35, 217–250.
   Web of Science ®Google Scholar
• Cunha, A.L., Zhou, J., and Do, M.N., 2006. The nonsubsampled contourlet transform: theory, design and applications. IEEE Transactions on Image Processing, 15, 3089–3101. doi:10.1109/TIP.2006.877507
   PubMed Web of Science ®Google Scholar
• Digital Globe, Inc. 2010. Radiometric use of WorldView-2 imagery, Technical Note, release date: 1 November 2010. http://Ww.digitalglobe.com/sites/default/files/Radiometric_Use_of_WorldView-2_Imagery%20(1).pdf
   Google Scholar
• Do, M.N. and Vetterli, M., 2005. The contourlet transform: an efficient directional multi-resolution image representation. IEEE Transactions on Image Processing, 14 (12), 2091–2106. doi:10.1109/TIP.2005.859376
   PubMed Web of Science ®Google Scholar
• Gabarda, S. and Cristóbal, G., 2005. On the use of a joint spatial-frequency representation for the fusion of multi-focus images. Pattern Recognition Letters, 26, 2572–2578. doi:10.1016/j.patrec.2005.06.003
   Web of Science ®Google Scholar
• Gabarda, S. and Cristóbal, G., 2007. Blind image quality assessment through anisotropy. Journal of the Optical Society of America A, 24, B42–B51. doi:10.1364/JOSAA.24.000B42
   Google Scholar
• Gabarda, S. and Cristóbal, G., 2012. No-reference image quality assessment through the von Mises distribution. Journal of the Optical Society of America A, 29 (10), 2058–2066. doi:10.1364/JOSAA.29.002058
   Google Scholar
• Gabarda, S., et al., 2009. Image de-noising and quality assessment through the R´enyi entropy. Proceedings SPIE, 7444–744419.
   Google Scholar
• Garguet-Duport, B., et al., 1996. The use of multi-resolution analysis and wavelet transform for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing, 62 (9), 1057–1066.
   Web of Science ®Google Scholar
• Ghosh, A. and Joshi, P.K., 2013. Assessment of pan-sharpened very high-resolution WorldView-2 images. International Journal of Remote Sensing, 34 (23), 8336–8359.
   Web of Science ®Google Scholar
• Gonzales, R.C. and Woods, R.E., 2003. Digital image processing. 2nd ed. Delhi, India: Pearson Education (Singapore) Pte. Ltd., Indian Branch. ISBN: 8178086298.
   Google Scholar
• González-Audícana, M., et al., 2005. Comparison between Mallat’s and the ‘à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. International Journal of Remote Sensing, 26, 595–614. doi:10.1080/01431160512331314056
   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, 1291–1299. doi:10.1109/TGRS.2004.825593
   Web of Science ®Google Scholar
• Gungor, O., 2008. Multi-Sensor Multi-Resolution Image Fusion. Ph.D dissertation. Purdue University West Lafayette, Indiana.
   Google Scholar
• Holschneider, M. and Tchamitchian, P., 1990. Regularite´ local de la function non-differentiable’ the Riemann. In: P.G. Lemarie´, ed. Les Ondelettes en 1989. Paris: Springer-Verlag, 102–124.
   Google Scholar
• Jia, Y. and Xiao, M., 2010. Fusion of PAN and multispectral images based on contourlet transform. In: ISPRS TC VII Symposium –100 Years ISPRS, Vienna, Austria: IAPRS, 38, Part 7B.
   Google Scholar
• Kang, T.J., Zhang, X.C., and Wang, H.Y., 2008. Assessment of the fused image of multi-spectral and panchromatic images of SPOT-5 in the investigation of geological hazards. Science in China Series E: Technological Sciences, 51, 144–153. doi:10.1007/s11431-008-6015-0
   Google Scholar
• Karathanassi, V., Kolokousis, P., and Ioannidou, S., 2007. A comparison study on fusion methods using evaluation indicators. International Journal of Remote Sensing, 28, 2309–2341. doi:10.1080/01431160600606890
   Web of Science ®Google Scholar
• Li, S., Li, Z., and Gong, J., 2010. Multivariate statistical analysis of measures for assessing the quality of image fusion. International Journal of Image and Data Fusion, 1, 47–66. doi:10.1080/19479830903562009
   Google Scholar
• Li, S., Yang, B., and Hu, J., 2011. Performance comparison of different multi-resolution transforms for image fusion. Information Fusion, 12, 74–84. doi:10.1016/j.inffus.2010.03.002
   Web of Science ®Google Scholar
• Mallat, S., 1989. A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674–693. doi:10.1109/34.192463
   Web of Science ®Google Scholar
• Mallat, S., 1999. A wavelet tour of signal processing. 2nd ed. San Diego, CA, USA: Academic Press, 1–637. ISBN: 012466606-1.
   Google Scholar
• Nikolakopoulos, K.G., 2008. Comparison of nine fusion techniques for very high resolution data. Photogrammetric Engineering & Remote Sensing, 74 (5), 647–659. doi:10.14358/PERS.74.5.647
   Web of Science ®Google Scholar
• Núñez, J., et al., 1999. Multi-resolution-based image fusion with additive wavelet decomposition. IEEE Transactions on Geo-science and Remote Sensing, 37, 1204–1211.
   Web of Science ®Google Scholar
• Pajares, G. and Cruz, M.J., 2004. A wavelet-based image fusion tutorial. Pattern Recognition, 37, 1855–1872. doi:10.1016/j.patcog.2004.03.010
   Web of Science ®Google Scholar
• Pohl, C. and Van Genderen, J.L., 1998. Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19, 823–854. doi:10.1080/014311698215748
   Web of Science ®Google Scholar
• Qiu, Z.C., 1990. The study on the remote sensing data fusion. Acta Geodaetica et Cartographica Sinica, 19 (4), 290–296.
   Google Scholar
• Rajput, U.K., Ghosh, S.K., and Kumar, A., 2014. Multi-sensor fusion of satellite images for urban information extraction using Pseudo-Wigner distribution. Journal of Applied Remote Sensing, 8, 083–668. doi:10.1117/1.JRS.8.083668
   Google Scholar
• Ranchin, T. and Wald, L., 1993. The wavelet transform for the analysis of remotely sensed images. International Journal of Remote Sensing, 14, 615–619. doi:10.1080/01431169308904362
   Web of Science ®Google Scholar
• Ranchin, T. and Wald, L., 2000. Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation. Photogrammetric Engineering and Remote Sensing, 66, 49–61.
   Web of Science ®Google Scholar
• Redondo, R., et al., 2009. Multi-focus image fusion using the Log-Gabor Transform and a multi-size windows technique. Information Fusion, 10, 163–171. doi:10.1016/j.inffus.2008.08.006
   Web of Science ®Google Scholar
• Richards, J.A. and Jia, X., 2006. Remote sensing digital image analysis, an introduction. 4th ed. Berlin, Heidelberg: Springer-Verlag. ISBN 10:3540251286.
   Google Scholar
• Roy, D., 2000. The impact of misregistration upon composited wide field of view satellite data and implications for change detection. IEEE Transactions on Geoscience and Remote Sensing, 38 (4), 2017–2032. doi:10.1109/36.851783
   Web of Science ®Google Scholar
• Shah, V.P., Younan, N.H., and King, R.L., 2007. Pan-sharpening via the contourlet transform. In: Proc. IEEE Int. Geo-science Remote Sensing. Symp. IGARSS 2007, 310–313.
   Google Scholar
• Shettigara, V.K., 1992. A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set. Photogrammetric Engineering and Remote Sensing, 58 (5), 561–567.
   Web of Science ®Google Scholar
• Shi, W., et al., 2005. Wavelet-based image fusion and quality assessment. International Journal of Applied Earth Observation and Geo-information, 6 (3), 241–251.
   Web of Science ®Google Scholar
• Shih, T.Y., 1995. The reversibility of six geometric color spaces. Photogrammetric Engineering and Remote Sensing, 61, 1223–1232.
   Web of Science ®Google Scholar
• Smith, A.R., 1978. Color gamut transform pairs. ACM SIGGRAPH Computer Graphics, 12, 12–19. doi:10.1145/965139
   Google Scholar
• Song, M., Chen, X., and Guo, P. 2009. A fusion method for multispectral and panchromatic images based on IHS and contourlet transformation. In: Proc. 10th Workshop on Image Analysis for Multimedia Interactive Services WIAMIS ’09, 77–80.
   Google Scholar
• Starck, J.L. and Murtagh, F., 1994. Image restoration with noise suppression using the wavelet transform. Astronomy Astrophysics, 288, 342–350.
   Web of Science ®Google Scholar
• Thomas, C. and Wald, L., 2007. Comparing distances for quality assessment of fused images. In: Proceedings of the 26th symposium of the European association of remote sensing laboratories, New Developments and Challenges in Remote Sensing. Rotterdam: Millpress, 101–111.
   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
• Valizadeh, S.A. and Ghassemian, H., 2012. Remote sensing image fusion using combining IHS and curvelet transform. In: 6th international symposium on telecommunications, (IST’2012), 6–8 November, Tehran, 1184–1189.
   Google Scholar
• Wald, L., 2000. Quality of high resolution synthesized images: is there a simple criterion. In: Third International Conference on Fusion Earth Data, January, Sophia Antipolos, 99–103.
   Google Scholar
• Wald, L., Ranchin, T., and 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
• Wang, Z. and Bovik, A.C., 2002. A universal image quality index. IEEE Signal Processing Letters, 9 (3), 81–84. doi:10.1109/97.995823
   Web of Science ®Google Scholar
• Wang, Z. and Ziou, D., 2005. A comparative analysis of image fusion methods. IEEE Transactions on Geo-Science and Remote Sensing, 43 (6), 1391–1402.
   Web of Science ®Google Scholar
• Wang, Z., et al., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13 (4), 600–612. doi:10.1109/TIP.2003.819861
   PubMed Web of Science ®Google Scholar
• Yang, B., Li, S., and Sun, F.M., 2007. Image fusion using non subsampled contourlet transform. IEEE International Conference on Image and Graphics, 719–724.
   Google Scholar
• Yang, X.-H. and Jiao, L.-C., 2008. Fusion algorithm for remote sensing images based on non sub-sampled contourlet transform. Acta Automatica Sinica, 34 (3), 274–281. doi:10.3724/SP.J.1004.2008.00274
   Google Scholar
• Yocky, D.A., 1995. Image merging and data fusion by means of the discrete two-dimensional wavelet transform. Journal of the Optical Society of America A, 12, 1834–1841. doi:10.1364/JOSAA.12.001834
   Google Scholar
• Yuhendra, Y., et al., 2012. Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data. International Journal of Applied Earth Observation and Geo-information, 18, 165–175. doi:10.1016/j.jag.2012.01.013
   Web of Science ®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 and Remote Sensing, 70, 657–661.
   Web of Science ®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 Quick Bird images. Information Fusion, 6 (3), 225–234. doi:10.1016/j.inffus.2004.06.009
   Google Scholar
• Zheng, T., et al., 2004. Digital image fusion. Xi’an: Xi’an Jiaotong University Press.
   Google Scholar
• Zhou, J., Civco, D.L., and Silander, J.A., 1998. A wavelet transform method to merge land sat TM and SPOT panchromatic data. International Journal of Remote Sensing, 19, 743–757. doi:10.1080/014311698215973
   Web of Science ®Google Scholar