结合多元经验模态分解和加权最小二乘滤波器的遥感图像融合
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摘要
为了提高多光谱图像的空间分辨能力的同时更大程度地保持光谱信息,提出了结合多元经验模态分解和加权最小二乘滤波器的遥感图像融合方法.多元经验模态分解解决了传统的基于单变量经验模态分解的遥感图像融合方法中多光谱图像的亮度分量和全色图像分解出的子图像频率不匹配导致融合图像空间细节信息缺失问题,加权最小二乘滤波器可以精确地估计出源图像的低频信息继而得到高频信息,减小了传统的经验模态分解方法估计的高频信息中混有低频成分而导致的光谱失真问题.将两者的优点结合,采用不同的融合规则得到的融合图像在空间细节和光谱信息的保持度较好.选取多组不同卫星数据进行仿真实验,并与结合多元经验模态分解和àtrous小波变换的方法以及基于加权最小二乘滤波器的遥感图像融合方法等方法进行比较,实验结果表明本文方法在光谱质量和空间分辨率方面都取得了很好的性能.
In order to improve the spatial resolution of multispectral images while maintaining spectral information to a greater extent,this paper proposes a remote sensing image fusion based on multivariate empirical mode decomposition and weighted least squares filter.On one hand,multivariate empirical mode decomposition solves the problem of spatial information distortion caused by the subimage frequency mismatch between the intensity component of the multispectral image and the panchromatic image in traditional remote sensing image fusion methods based on univariate empirical mode decomposition.On the other hand,remote sensing image fusion based on multivariate empirical mode decomposition usually suffer from serious spectral distortions due to the detail information contains low frequency components.To overcome these defects,the weighted least squares filter can estimate low frequency information of source image accurately and obtain the high-frequency information subsequently.Combine the advantages of both,the fused image obtained by different fusion rules has better spatial detail and spectral information retention.In this paper,different satellite data are selected for simulation experiments,and compared with other methods such as based multivariate empirical mode decomposition andàtrous wavelet transform and based on weighted least squares filter,the results of experiment achieve good performance in both spectral and spatial qualities.
In order to improve the spatial resolution of multispectral images while maintaining spectral information to a greater extent,this paper proposes a remote sensing image fusion based on multivariate empirical mode decomposition and weighted least squares filter.On one hand,multivariate empirical mode decomposition solves the problem of spatial information distortion caused by the subimage frequency mismatch between the intensity component of the multispectral image and the panchromatic image in traditional remote sensing image fusion methods based on univariate empirical mode decomposition.On the other hand,remote sensing image fusion based on multivariate empirical mode decomposition usually suffer from serious spectral distortions due to the detail information contains low frequency components.To overcome these defects,the weighted least squares filter can estimate low frequency information of source image accurately and obtain the high-frequency information subsequently.Combine the advantages of both,the fused image obtained by different fusion rules has better spatial detail and spectral information retention.In this paper,different satellite data are selected for simulation experiments,and compared with other methods such as based multivariate empirical mode decomposition andàtrous wavelet transform and based on weighted least squares filter,the results of experiment achieve good performance in both spectral and spatial qualities.
引文
[1] PEI Xiao-peng.Remote sensing image fusion based on sparse representation[D].Taiyuan:Taiyuan University of Technology,2018.裴晓鹏.基于稀疏表示的遥感图像融合方法[D].太原:太原理工大学,2018.
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[12] SUN Yan,ZHAO Chun-hui,JIANG Ling.Remote sensing image fusion algorithm based on NSCT and PCA transform-domain[J].Journal of Shen-yang University of Technology,2011(3):308-314.孙岩,赵春晖,江凌.基于NSCT和PCA变换域的遥感图像融合算法[J].沈阳工业大学学报,2011(3):308-314.
[13] WU Xiao-yan,CHAI Jing,LIU Fan,et al.Remote sensing image fusion based on minimum hausdorff distance and nonsampled shearlet transform[J].Acta Photonica Sinca,2018,47(2):0210002.武晓焱,柴晶,刘帆,等.基于最小Hausdorff距离和NSST的遥感图像融合[J].光子学报,2018,47(2):0210002.
[14] YIN Ming,PANG Ji-yong,WEI Yuan-zheng,et al.Remote sensing image fusion based on non-subsampled dual-tree complex contourlet transform and sparse representation[J].Acta Photonica Sinca,2016,45(1):0110002.殷明,庞纪勇,魏远远,等.结合NSDTCT和稀疏表示的遥感图像融合[J].光子学报,2016,45(1):0110002.
[15]HUI B I,ZHANG B C,WEN H.Matrix completion-based distributed compressive sensing for polarimetric SAR tomography[J].Science China Information Sciences,2015,58(11):119301-119301.
[16] LIU Fan,PEI Xiao-peng,CHEN Ze-hua,et al.Remote sensing image fusion based on optimized dictionary learning[J].Journal of Electronics&Information Technology,2018(12):2804-2811.刘帆,裴晓鹏,陈泽华,等.基于优化字典学习的遥感图像融合方法[J].电子与信息学报,2018(12):2804-2811.
[17] TEO T A.Pyramid-based image empirical mode decomposition for the fusion of multispectral and panchromatic images[J].Eurasip Journal on Advances in Signal Processing,2012,2012(1):4.
[18] YANG Jin-ku.Research on image fusion algorithms using bidimensional empirical mode mode decomposition[D].Xian:Northwestern Polytechnical University,2016.杨金库.基于二维经验模态分解的图像融合算法研究[D].西安:西北工业大学,2016.
[19] SONG Y,WU W,LIU Z,et al.An adaptive pansharpening method by using weighted least squares filter[J].IEEE Geoscience&Remote Sensing Letters,2016,13(1):18-22.
[20] ABDULLAH S M U,REHMAN N U,KHAN M M,et al.A multivariate empirical mode decomposition based approach to pansharpening[J].IEEE Transactions on Geoscience&Remote Sensing,2015,53(7):3974-3984.
[21] HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings A,1998,454(1971):903-995.
[22] YUHAS R H,GOETZ A F H,BOARDMAN J W.Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper algorithm(SAM)algorithm[C].JPL Airborne Geosci,1992:147-149.
[23] WANG Z,BOVIK A C.A universal image quality index[J].IEEE Signal Processing Letters,2002,9(3):81-84.
[24] REHMAN N,KHAN M M,SOHAIB M I,et al.Image fusion using multivariate and multidimensional EMD[C].IEEE International Conference on Image Processing,2015:5112-5116.
[25] LIU Fan.Remote sensing image fusion based on wavelet kernel filter and sparse representation[D].Xi′an:Xidian University,2014.刘帆.基于小波核滤波器和稀疏表示的遥感图像融合[D].西安:西安电子科技大学,2014.
[2] THOMAS C,RANCHIN T,WALD L,et al.Synthesis of multispectral images to high spatial resolution:a critical review of fusion methods based on remote sensing physics[J].IEEE Transactions on Geoscience&Remote Sensing,2008,46(5):1301-1312.
[3] GHASSEMIAN H.A review of remote sensing image fusion methods[J].Information Fusion,2016,32(PA):75-89.
[4] LEUNG Y,LIU J,ZHANG J.An improved adaptive intensity-hue-saturation method for the fusion of remote sensing images[J].IEEE Geoscience&Remote Sensing Letters,2013,11(5):985-989.
[5] SHAH V P,YOUNAN N H,KING R L.An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets[J].IEEE Transactions on Geoscience&Remote Sensing,2008,46(5):1323-1335.
[6] VIVONE G,ALPARONE L,CHANUSSOT J,et al.A critical comparison among pansharpening algorithms[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(5):2565-2586.
[7] WANG Hai-hui,LU Yan-sheng,CHEN Min-jiang.Remote sensing image fusion by using discrete multiwavelet transform[J].Journal of Huazhong University of Science&Technology,2006,34(8):7-9.王海晖,卢炎生,陈闽江.基于离散多小波变换的遥感图像融合方法[J].华中科技大学学报(自然科学版),2006,34(8):7-9.
[8] XU H,LIU Z,PENG G.Image fusion method based on total variation andàtrous wavelet[C].IEEE International Conference on Signal Processing,Communications and Computing,IEEE,2011:1-6.
[9] ZHANG Qiang,GUO Bao-long.Fusion of multifocus images based on the nonsubsampled contourlet transform[J].Acta Photonica Sinica,2008,37(4):838-843.张强,郭宝龙.一种基于非下采样Contourlet变换多聚焦图像融合算法[J].光子学报,2008,37(4):838-843.
[10] STARCK J L,FADILI J,MURTAGH F.The undecimated wavelet decomposition and its reconstruction[J].IEEE Transactions on Image Processing,2007,16(2):297-309.
[11] LIU Jian,LEI Ying-jie,XING Ya-qiong,et al.Image fusion method based on improved NSST transform[J].Control&Decision,2017,32(2):275-280.刘健,雷英杰,邢雅琼,等.基于改进型NSST变换的图像融合方法[J].控制与决策,2017,32(2):275-280.
[12] SUN Yan,ZHAO Chun-hui,JIANG Ling.Remote sensing image fusion algorithm based on NSCT and PCA transform-domain[J].Journal of Shen-yang University of Technology,2011(3):308-314.孙岩,赵春晖,江凌.基于NSCT和PCA变换域的遥感图像融合算法[J].沈阳工业大学学报,2011(3):308-314.
[13] WU Xiao-yan,CHAI Jing,LIU Fan,et al.Remote sensing image fusion based on minimum hausdorff distance and nonsampled shearlet transform[J].Acta Photonica Sinca,2018,47(2):0210002.武晓焱,柴晶,刘帆,等.基于最小Hausdorff距离和NSST的遥感图像融合[J].光子学报,2018,47(2):0210002.
[14] YIN Ming,PANG Ji-yong,WEI Yuan-zheng,et al.Remote sensing image fusion based on non-subsampled dual-tree complex contourlet transform and sparse representation[J].Acta Photonica Sinca,2016,45(1):0110002.殷明,庞纪勇,魏远远,等.结合NSDTCT和稀疏表示的遥感图像融合[J].光子学报,2016,45(1):0110002.
[15]HUI B I,ZHANG B C,WEN H.Matrix completion-based distributed compressive sensing for polarimetric SAR tomography[J].Science China Information Sciences,2015,58(11):119301-119301.
[16] LIU Fan,PEI Xiao-peng,CHEN Ze-hua,et al.Remote sensing image fusion based on optimized dictionary learning[J].Journal of Electronics&Information Technology,2018(12):2804-2811.刘帆,裴晓鹏,陈泽华,等.基于优化字典学习的遥感图像融合方法[J].电子与信息学报,2018(12):2804-2811.
[17] TEO T A.Pyramid-based image empirical mode decomposition for the fusion of multispectral and panchromatic images[J].Eurasip Journal on Advances in Signal Processing,2012,2012(1):4.
[18] YANG Jin-ku.Research on image fusion algorithms using bidimensional empirical mode mode decomposition[D].Xian:Northwestern Polytechnical University,2016.杨金库.基于二维经验模态分解的图像融合算法研究[D].西安:西北工业大学,2016.
[19] SONG Y,WU W,LIU Z,et al.An adaptive pansharpening method by using weighted least squares filter[J].IEEE Geoscience&Remote Sensing Letters,2016,13(1):18-22.
[20] ABDULLAH S M U,REHMAN N U,KHAN M M,et al.A multivariate empirical mode decomposition based approach to pansharpening[J].IEEE Transactions on Geoscience&Remote Sensing,2015,53(7):3974-3984.
[21] HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings A,1998,454(1971):903-995.
[22] YUHAS R H,GOETZ A F H,BOARDMAN J W.Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper algorithm(SAM)algorithm[C].JPL Airborne Geosci,1992:147-149.
[23] WANG Z,BOVIK A C.A universal image quality index[J].IEEE Signal Processing Letters,2002,9(3):81-84.
[24] REHMAN N,KHAN M M,SOHAIB M I,et al.Image fusion using multivariate and multidimensional EMD[C].IEEE International Conference on Image Processing,2015:5112-5116.
[25] LIU Fan.Remote sensing image fusion based on wavelet kernel filter and sparse representation[D].Xi′an:Xidian University,2014.刘帆.基于小波核滤波器和稀疏表示的遥感图像融合[D].西安:西安电子科技大学,2014.