Please wait a minute...
J4  2013, Vol. 47 Issue (7): 1258-1266    DOI: 10.3785/j.issn.1008-973X.2013.07.019
Fusion of panchromatic image and multi-spectral image based on
SVR and Bayesian method 
HU Gen-sheng1,2, BAO Wen-xia1,2, LIANG Dong1,2, ZHANG Wei1
1. MOE Key Laboratory of IC & SP, Anhui University, Hefei 230039, China;2. School of Electronics and
Information Engineering, Anhui University, Hefei 230601, China
Download:   PDF(0KB) HTML
Export: BibTeX | EndNote (RIS)      


The fusion  images with high spatial resolution and high spectral resolution can be obtained by fusing panchromatic images and multi-spectral images. Support vector value contourlet transform constructed by using support vector regression model was used to decompose source images at multi-scale, multi-direction and multi-resolution. The algorithm of fusing panchromatic image and multi-spectral image was derived at different levels by using Bayesian method. By utilizing the strong learning ability of support vector regression and the relationship of multi-spectral image with panchromatic image, the super-resolved multi-spectral image was reconstructed to resolve the problem of coincident resolution of images to be fused. Experimental results show that the fused image obtained by the method not only has  high spatial resolution, but also preserves the spectral characteristics of the multi-spectral images. The fusion performance of the method is better than traditional image fusion methods.

Published: 01 July 2013
CLC:  TP 391  
Cite this article:

HU Gen-sheng, BAO Wen-xia, LIANG Dong, ZHANG Wei. Fusion of panchromatic image and multi-spectral image based on
SVR and Bayesian method . J4, 2013, 47(7): 1258-1266.

URL:     OR



[1] CHEN S, ZHANG R, SU H, et al. SAR and multispectral image fusion using generalized IHS transform based on à Trous wavelet and EMD decompositions [J]. IEEE Sensors Journal, 2010, 10(3): 737-745.
[2] SALVADEO D H P, MASCARENHAS N D A, MOREIRA J, et al. Improving face recognition performance using RBPCA maxlike and information fusion [J]. Computing in Science and Engineering, 2011, 13(3): 14-21.
[3] TU T M, CHENG W C, CHANG C P, et al. Best tradeoff for high-resolution image fusion to preserve spatial details and minimize color distortion [J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(2): 302-306.
[4] RAHMAN M M, CSAPLOVICS E. Examination of image fusion using synthetic variable ratio (SVR) technique [J]. International Journal of Remote Sensing, 2007, 28(15): 3413-3424.
[5] CHENG G, GUO L, ZHAO T. An improved multi-focus image fusion method based on wavelet transform [C]∥ Proceedings of ICEEE 2010. Henan: IEEE, 2010: 1-4.
[6] IOANNIDOU S, KARATHANASSI V. Investigation of the dual-tree complex and shift-invariant discrete wavelet transforms on quickbird image fusion [J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(1): 166-170.
[7] KONG W W, LEI Y J, LEI Y, et al. Image fusion technique based on non-subsampled contourlet transform and adaptive unit-fast-linking pulse-coupled neural network [J]. IET Image Processing, 2011, 5(2): 113-121.
[8] YANG S, WANG M, LU Y, et al. Fusion of multiparametric SAR images based on SW-nonsubsampled contourlet and PCNN [J]. Signal Processing, 2009, 89(12): 2596-2608.
[9] CHANG X, JIAO L, LIU F, et al. Multicontourlet-based adaptive fusion of infrared and visible remote sensing images [J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(3): 549-553.
[10] LI X, HE Y, ZHAN X. Edge-enhancement EPMA image fusion based on directionlet transform [C]∥3rd International Symposium on Intelligent Information Technology Application. Nanchang: IEEE, 2009: 180-183.
[11] HARDIE R C, EISMANN M T, WILSON G L. MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor [J]. IEEE Transactions on Image Processing, 2004, 13(9): 1174-1184.
[12] FASBENDER D, RADOUX J, BOGAERT P. Bayesian data fusion for adaptable image pansharpening [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6): 1847-1857.
[13] ZHANG Y, DE BACKER S, SCHEUNDERS P. Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(11): 3834-3843.
[14] 胡根生, 邓飞其. 具有多分段损失函数的多输出支持向量机回归[J]. 控制理论与应用, 2007, 24(5): 711-714.
HU Gen-sheng, DENG Fei-qi. Multi-output support vector regression with piecewise loss function [J]. Control Theory and Applications, 2007,24(5): 711-714.
[15] SCHOLKOPT B, SMOLA A J. Learning with Kernels[M]. Cambridge:  MIT, 2002.
[16] 胡根生, 梁栋, 孔颉. 基于支持向量值轮廓波变换的遥感影像融合[J]. 电子学报, 2010, 38(6): 1287-1291.
HU Gen-sheng, LIANG Dong, KONG Jie. Remote sensing image fusion based on support vector value contourlet transform [J]. Acta Electronic Sinica, 2010, 38(6): 1287-1291.
[17] 胡根生, 梁栋, 黄林生. 基于支持向量值轮廓波变换的遥感图像去噪[J]. 系统工程与电子技术, 2011, 33(7): 1658-1663.
HU Gen-sheng, LIANG Dong, HUANG Lin-sheng. Remote sensing image denoising based on support vector value contourlet transform [J]. Systems Engineering and Electronics, 2011, 33(7): 1658-1663.
[18]ZHENG S, SHI W, LIU J, et al. Remote sensing image fusion using multiscale mapped LS-SVM [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(5): 1313-1322.
[19] SHEN H, ZHANG L, HUANG B, et al.A MAP approach for joint motion estimation, segmentation, and super resolution [J]. IEEE Transactions on Image Processing, 2007, 16(2): 479-490.
[20] DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm [J]. Journal of the Royal Statistical Society, Series B, 1977, 39 (1): 1-22.
[21] SUNDARESHAN M K, BHATTACHARJEE S. Superresolution of passive millimeter-wave images using a combined maximum-likelihood optimization and projection-onto-convex- sets approach [J]. Proceeding of SPIE, 2001, 4373: 105-116.
[22] WANG Z, WANG W. Fast and adaptive method for SAR superresolution imaging based on point scattering model and optimal basis selection [J]. IEEE Transactions on Image Processing, 2009, 18(7):1477-1486.
[23] ELBAKARY M, ALAM M. Superresolution construction of multispectral imagery based on local enhancement [J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(2): 276-279.
[24] JI H, FERMULLER C. Robust wavelet-based super-resolution reconstruction: theory and algorithm [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(4): 6496-60.
[25] YAP K H, HE Y, TIAN Y, et al. A nonlinear L1-norm approach for joint image registration and super-resolution [J]. IEEE Signal Processing Letters, 2009, 16(11): 981-984.
[26] FREEMAN W T, JONES T R, PASZTOR E C. Example based superresolution [J]. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65.
[27] CHAN T, ZHANG J, PU J, et al. Neighbor embedding based super-resolution algorithm through edge detection and feature selection [J]. Pattern Recognition Letters, 2009, 30(5): 494-502.
[28] MIRAVET C, RODRIGUEZ F B. A two-step neural-network based algorithm for fast image super-resolution [J]. Image and Vision Computing, 2007, 25(9): 1449-1473.
[29] TAKEDA H, MILANFAR P, PROTTER M, et al. Super-resolution without explicit subpixel motion estimation [J]. IEEE Transactions on Image Processing, 2009, 18(9): 1958-1975.
[30] NI K S, NGUYEN T Q. Image superresolution using support vector regression [J]. IEEE Transactions on Image Processing, 2007, 16(6): 1596-1610.
[31] LIN Z, HE J, TANG X, et al. Limits of learning-based superresolution algorithms\
[J\]. International Journal of Computer Vision, 2008, 80(3):406-420.
[32] ELBAKARY M, ALAM M. Superresolution construction of multispectral imagery based on local enhancement [J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(2): 276-279.
[33] FASBENDER D, TUIA D, BOGAERT P, et al. Support-based implementation of Bayesian data fusion for spatial enhancement: applications to ASTER thermal images [J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 598-602.

[1] ZHAO Jian-jun, WANG Yi, YANG Li-bin. Threat assessment method based on time series forecast[J]. J4, 2014, 48(3): 398-403.
[2] ZHANG Tian-yu, FENG Hua-jun, XU Zhi-hai, LI Qi, CHEN Yue-ting. Sharpness metric based on histogram of strong edge width[J]. J4, 2014, 48(2): 312-320.
[3] LIU Zhong, CHEN Wei-hai, WU Xing-ming, ZOU Yu-hua, WANG Jian-hua. Salient region detection based on stereo vision[J]. J4, 2014, 48(2): 354-359.
[4] CUI Guang-mang, ZHAO Ju-feng,FENG Hua-jun, XU Zhi-hai,LI Qi, CHEN Yue-ting. Construction of fast simulation model for degraded image by inhomogeneous medium[J]. J4, 2014, 48(2): 303-311.
[5] WANG Xiang-bing,TONG Shui-guang,ZHONG Wei,ZHANG Jian. Study on  scheme design technique for hydraulic excavator's structure performance based on extension reuse[J]. J4, 2013, 47(11): 1992-2002.
[6] WANG Jin, LU Guo-dong, ZHANG Yun-long. Quantification-I theory based IGA and its application[J]. J4, 2013, 47(10): 1697-1704.
[7] LIU Yu, WANG Guo-jin. Designing  developable surface pencil through  given curve as its common asymptotic curve[J]. J4, 2013, 47(7): 1246-1252.
[8] WU Jin-liang, HUANG Hai-bin, LIU Li-gang. Texture details preserving seamless image composition[J]. J4, 2013, 47(6): 951-956.
[9] CHEN Xiao-hong,WANG Wei-dong. A HDTV video de-noising algorithm based on spatial-temporal filtering[J]. J4, 2013, 47(5): 853-859.
[10] ZHU Fan , LI Yue, JIANG Kai, YE Shu-ming, ZHENG Xiao-xiang. Decoding of rat’s primary motor cortex by partial least square[J]. J4, 2013, 47(5): 901-905.
[11] WU Ning, CHEN Qiu-xiao, ZHOU Ling, WAN Li. Multi-level method of optimizing vector graphs converted from remote sensing images[J]. J4, 2013, 47(4): 581-587.
[12] JI Yu, SHEN Ji-zhong, SHI Jin-he. Automatic ocular artifact removal based on blind source separation[J]. J4, 2013, 47(3): 415-421.
[13] WANG Xiang, DING Yong. Full reference image quality assessment based on Gabor filter[J]. J4, 2013, 47(3): 422-430.
[14] TONG Shui-guang, WANG Xiang-bing, ZHONG Wei, ZHANG Jian. Dynamic optimization design for rigid landing leg of crane
based on BP-HGA
[J]. J4, 2013, 47(1): 122-130.
[15] LIU Fang, SUN Yun, YANG Geng, LIN Hai. Visualization of social network based on particle swarm optimization[J]. J4, 2013, 47(1): 37-43.