Efficient image fusion method using improved Bi-dimensional Empirical Mode Decomposition

References

• 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 (4), 249–263. doi:10.1016/j.isprsjprs.2007.05.009.
   Web of Science ®Google Scholar
• Beaulieu, M., Foucher, S., and Gagnon, L. 2003. Multi-spectral image resolution refinement using stationary wavelet transform. In: Proc. IEEE Geoscience and Remote Sensing Symp, Vol. 6. Toulouse, France: IEEE, 4032–4034
   Google Scholar
• Celebi, A.T. and Erturk, S. 2010. Empirical mode decomposition based visual enhancement of underwater images. 2nd International Conference on Image Processing Theory, Tools and Applications (2010). doi:10.1109/ipta.2010.5586758.
   Google Scholar
• Chang, M., You, X., and Cao, Z. 2019. Bidimensional empirical mode decomposition for SAR image feature extraction with application to target recognition. IEEE Access, 1–1. doi:10.1109/access.2019.2941397.
   Google Scholar
• Chavez, P.S., Sides, S.C., and Anderson, J.A., 1991. Comparison of three different methods to merge multiresolution and multispectral data: landsat TM and SPOT panchromatic. Photogramm Eng Remote Sens, 57 (3), 295–303.
   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:http://dx.doi.org/10.1109/LGRS.2005.845313
   Web of Science ®Google Scholar
• Damerval, C., Meignen, S., and Perrier, V., 2005. A fast algorithm for bidimensional EMD. IEEE Signal Processing Letters, 12 (10). doi:10.1109/LSP.2005.855548
   Web of Science ®Google Scholar
• Davis, C.H. and Wang, X., 2002. Urban land cover classification from high resolution multispectral IKONOS imagery. in Proc. IEEE Int. Geosciences and Remote Sensing Symposium, Toronto, Canada, Vol. 2, pp. 1204–1206, IEEE
   Google Scholar
• Denipote, J.G. and Paiva, S.V. 2008. A fourier transform-based approach to fusion high spatial resolution remote sensing images. in Sixth Indian Conf. Computer Vision, Graphics and Image Processing, pp. 179–186, IEEE, Bhubaneswar, India.
   Google Scholar
• Fanelli, A., Leo, A., and Ferri, M. 2001. Remote sensing images data fusion: a wavelet transform approach for urban analysis. in Proc. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas, Rome, Italy, pp. 112–116.
   Google Scholar
• Garzelli, A. 2002. Possibilities and limitations of the use of wavelets in image fusion. in IEEE Int. Geoscience and Remote Sensing Symp, Vol. 1, pp. 66–68, IEEE, Toronto, Canada.
   Google Scholar
• Ghellab, A.M.R. and Belbachir, M.F., 2013. Efficient image fusion method based on the fourier transform by introducing sensor spectral response. Journal of Applied Remote Sensing, 7 (1), 073552. doi:10.1117/1.jrs.7.073552.
   Google Scholar
• Gonzalez-Audicana, M., et al., 2004. Fusion of multispectral and panchromatic images using improved HIS and PCA mergers based on wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society, 42 (6), 1291–1299. doi:10.1109/TGRS.2004.825593
   Web of Science ®Google Scholar
• González-Audícana, M., et al., 2002. Fusion of different spatial and spectral resolution images: development, application and comparison of new methods based on wavelets. in Proc. Int. Symp. Recent Advances in Quantitative Remote Sensing, Torrent, Spain, pp. 228–237.
   Google Scholar
• González-Audícana, M., et al., 2005. Comparison between the 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 (3), 595–614. doi:10.1080/01431160512331314056
   Web of Science ®Google Scholar
• He, Z., et al., 2013. Multivariate gray model-based BEMD for hyperspectral image classification. IEEE Transactions on Instrumentation and Measurement, 62 (5), 889–904. 10.1109/tim.2013.2246917
   Web of Science ®Google Scholar
• Hem Hongyuan Wang, L., 2006. Spatial-variant image filtering based on bidimensional empirical mode decomposition. The 18th International Conference on Pattern Recognition (ICPR’06). IEEE 2006. doi:10.1109/icpr.2006.1070.
   Google Scholar
• Hou, W.L., et al., 2019. Random noise reduction in seismic data by using bidimensional empirical mode decomposition and shearlet transform. Institute of Electrical and Electronics Engineers Access, 7, 71374–71386. doi:10.1109/access.2019.2920021
   Google Scholar
• Huang, N.E., et al., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, 454, 903–995. doi:10.1098/rspa.1998.0193
   Google Scholar
• Jean Christophe Cexus, 2005. Analyse des signaux non-sationnaires par transformation de Huang, Opérateur de Teager-Kaizer, et transformation de Huang-teager THT. Thesis (PhD). University of Rennes 1, France.
   Google Scholar
• Jian, W., Jixian, Z., and Zhengjun, L., 2008. EMD based multi-scale model for high resolution image fusion. Geo-Spatial Information Science, 11 (1), 31–37. doi:10.1007/s11806-007-0150-9.
   Google Scholar
• Kim, D., Park, M., and Hee-Seok, O., 2012. Bidimensional statistical empirical mode decomposition. IEEE Signal Processing Letters, 19 (4), 191–194. doi:10.1109/lsp.2012.2186566.
   Web of Science ®Google Scholar
• Kishore, D.D., Gopal, R.K., and Prakash, A., 2001. Improvement of effective spatial resolution of thermal infrared data for urban land use classification. in Proc. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas, Rome, Italy, pp. 332–336, IEEE.
   Google Scholar
• Ling, Y., et al., 2007. FFT-enhanced HIS transform method for fusing high-resolution satellite Images. ISPRS Journal of Photogrammetry and Remote Sensing, 61 (6), 381–392. doi:10.1016/j.isprsjprs.2006.11.002
   Web of Science ®Google Scholar
• Li, X., Su, J., and Yang, L., 2019. Building detection in SAR images based on bi-dimensional empirical mode decomposition algorithm. IEEE Geoscience and Remote Sensing Letters, 1–5. doi:10.1109/lgrs.2019.2928965.
   Google Scholar
• Liu, Z. and Peng, S., 2005. Boundary processing of bidimensional EMD using texture synthesis. IEEE Signal Processing Letters, 12 (1), 33–36. 10.1109/lsp.2004.839700
   Web of Science ®Google Scholar
• Ming, L. and Shunjun, W. 2003. A new image fusion algorithm based on wavelet transform. in Proc. Fifth Int. Conf. Computational Intelligence and Multimedia Applications, Xi’an, China, p. 154, IEEE.
   Google Scholar
• Nuñez, J., et al., 1999. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society, 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: A Publication of the IEEE Geoscience and Remote Sensing Society, 43 (10), 2376–2385. doi:10.1109/TGRS.2005.856106
   Web of Science ®Google Scholar
• Pan, J.J., Tang, Y.Y., and Zhang, D., 2010. A fractal-based BEMD method for image texture analysis. IEEE International Conference on Systems, Man and Cybernetics (2010). doi:10.1109/icsmc.2010.5642020.
   Google Scholar
• Qiao, L.H., et al., 2008. A novel image fusion algorithm based on 2D EMD and HIS. in Proc. Seventh Int. Conf. Machine Learning and Cybernetics, pp.4040–4044, IEEE, San Diego, California.
   Google Scholar
• Raptis, V.S., et al., 1998. Assessment of different data fusion methods for the classification of an urban environment. in Proc. 2nd Conference, Fusion of Earth Data: Merging Point Measurements, Raster Maps and Remotely Sensed Images, <.I.I.A.I.<. Ranchin and <.I.I.A.I.<. Wald, eds, pp. 167–182, Sophia Antipolis, France.
   Google Scholar
• Shettigara, V.K., 1992. A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set. Photogramm Eng Remote Sens, 58 (5), 561–567.
   Web of Science ®Google Scholar
• Thomasl, C., 2006. Fusion D’images de résolutions spatiales différentes. Thèse,Ecole des Mines de Paris. Thesis (PhD). Paris School of Mines, France.
   Google Scholar
• Tsai, V.J.D. 2003. Frequency-based fusion of multiresolution images. in Proc. IEEE Int. Geoscience and Remote Sensing Symp,Vol. 6, pp. 3916–3919, IEEE, Toulouse, France.
   Google Scholar
• Tsai, V.J.D. 2004. Evaluation of multiresolution image fusion algorithms. in Proc. IEEE Int. Geoscience and Remote Sensing Symp, Vol. 1, pp. 3665–3667, IEEE, Anchorage, Alaska.
   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
• Vaiopoulos, D., Nikolakopoulos, K., and Skianis, G. 2001. A comparative study of resolution merge techniques and their efficiency in processing image of urban areas. in Proc. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas, Rome, Italy, pp. 270–274, IEEE.
   Google Scholar
• Wald, L. and Ranchin, T., 2001. Data fusion for a better knowledge of urban areas. in Proc. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion Over Urban Areas, Rome, Italy, pp. 127–132, IEEE.
   Google Scholar
• Wald, L. and Ranchin, T., 2003. The ARSIS concept in image fusion: an answer to users needs. in Proc. Sixth Int. Conf. Information Fusion, pp. 168–173, IEEE, Cairns, Queensland, Australia.
   Google Scholar
• Wald, L., Ranchin, T., and Mangolini, M., 1997. Fusion of satellite images of different spatial resolution: assessing the quality of resulting images. Photogramm Eng Remote Sens, 63 (6), 691–699.
   Web of Science ®Google Scholar
• Wang, J., et al., 2007. An EMD-HIS model for high resolution image fusion. Proceedings of SPIE, 6752, 675209. doi:10.1117/12.760475
   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
• Weng, B., Blanco-Velasco, M., and Barner, K.E., 2006. ECG denoising based on the empirical mode decomposition. International Conference of the 28th IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2006.259340. New York City, USA, Aug 30-Sept 3.
   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, 12 (9), 1834–1841. doi:http://dx.doi.org/10.1364/JOSAA.12.001834.
   Web of Science ®Google Scholar
• Zang, Y. and Wang, R., 2004. Multi-resolution and multi-spectral image fusion for urbanobject extraction. in Proc. 20th ISPRS Congress, Geo-Imagery Bridging Continents, Istanbul, Turkey, pp. 960–966.
   Google Scholar
• Zhangl, Y., 2002. Problems in the fusion of commercial high-resolution satellite images as well as landsat 7 images and initial solutions. In: Inte archives of photogrammetry and remote sensing, GeoSpatial theory, processing and applications, ISRP, Ottawa, Vol. 34.
   Google Scholar