基于协同经验小波变换的遥感图像融合
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摘要
针对多源遥感图像的融合问题,提出了一种基于协同经验小波变换的遥感图像融合方法。该算法首先对多源图像进行主成分分析获得共像;然后,对共像的强度分量做经验小波变换获得滤波器组;再利用这组滤波器对多光谱图像的强度分量和全色图像进行多尺度表示;最后经逆变换得到融合图像。该算法因采用协同自适应分解方法,有利于源图像高频与低频信息的分离,有效提高了遥感融合图像的清晰度。通过使用QuickBird卫星数据验证了算法的有效性,视觉感知和客观评价标准均表明该算法比其他同类算法有更好的优越性。
A remote sensing image fusion method based on cooperative empirical wavelet transform to fuse multi-source remote sensing image is proposed. Firstly,the algorithm performed principal component analysis on multi-source images to obtain a common image. Secondly,empirical wavelet transform was performed on intensity components of the common image to obtain filter banks. Then these filters were used to represent multi-spectral image intensity components and panchromatic images in multiscale.Finally,fusion image was obtained by inverse transformation. The algorithm adopts the cooperative adaptive decomposition method, which is beneficial to separate high frequency and low frequency information of the source image,and effectively improve the clarity of the remote sensing fusion image.QuickBird satellite data verifies the effectiveness of the algorithm. Visual perception and objective evaluation criteria indicate that has better advantages than other similar algorithms.
A remote sensing image fusion method based on cooperative empirical wavelet transform to fuse multi-source remote sensing image is proposed. Firstly,the algorithm performed principal component analysis on multi-source images to obtain a common image. Secondly,empirical wavelet transform was performed on intensity components of the common image to obtain filter banks. Then these filters were used to represent multi-spectral image intensity components and panchromatic images in multiscale.Finally,fusion image was obtained by inverse transformation. The algorithm adopts the cooperative adaptive decomposition method, which is beneficial to separate high frequency and low frequency information of the source image,and effectively improve the clarity of the remote sensing fusion image.QuickBird satellite data verifies the effectiveness of the algorithm. Visual perception and objective evaluation criteria indicate that has better advantages than other similar algorithms.
引文
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[16]Cheng J,Liu H,Liu T,et al.Remote sensing image fusion via wavelet transform and sparse representation[J].Isprs Journal of Photogrammetry&Remote Sensing,2015,104:158-173.
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[19]Li W,Hu X,Du J,et al.Adaptive remote-sensing image fusion based on dynamic gradient sparse and average gradient difference[J].International Journal of Remote Sensing,2017,38(23):7316-7332.
[20]Wang Q,Meng Z,Li X.Locality adaptive discriminant analysis for spectral-spatial classification of hyperspectral images[J].IEEE Geoscience&Remote Sensing Letters,2017,14(11):2077-2081.
[21]Gilles J.Empirical wavelet transform[J].IEEETransactions on Signal Processing,2013,61(16):3999-4010.
[22]陈善学,唐义嫄.基于混沌系统的RGB彩色图像三重置乱算法[J].重庆邮电大学学报:自然科学版,2018,30(6):812-818.Chen Shan-xue,Tang Yi-yuan.Triple scrambling algorithm for RGB color image based on chaotic system[J].Chongqing University of Posts and Telecommunications(Natural Science Edition),2018,30(6):812-818.
[23]Gilles J,Tran G,Osher S.2D empirical transforms.wavelets,ridgelets and curvelets revisited[J].Siam Journal on Imaging Sciences,2014,7(7):157-186.
[24]刘磊,张红,王莎.基于小波变换的全局能量图像融合算法[J].吉林大学学报:工学版,2009,39(增刊1):232-236.Liu Lei,Zhang Hong,Wang Sha.Global energy image fusion algorithm based on wavelet transform[J].Journal of Jilin University(Engineering and Technology Edition),2009,39(Supl.1):232-236.
[2]Yang Y,Wan W,Huang S,et al.Remote sensing image fusion based on adaptive IHS and multiscale guided filter[J].IEEE Access,2017,4:4573-4582.
[3]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.
[4]Zhang Y,Hong G.An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images[J].Information Fusion,2005,6(3):225-234.
[5]Shah V P,Younan N H,King R L.An efficient pansharpening method via a combined adaptive PCA approach and contourlets[J].IEEE Transactions on Geoscience&Remote Sensing,2008,46(5):1323-1335.
[6]陶旭婷,和红杰,陈帆,等.基于局部相关性的遥感图像全色锐化算法[J].光子学报,2014,43(3):310003.Tao Xu-ting,He Hong-jie,Chen Fan,et al.Panchromatic sharpening algorithm based on local correlation for remote sensing images[J].Acta Photonica Sinica,2014,43(3):310003.
[7]Pajares G,Cruz J M D L.A wavelet-based image fusion tutorial[J].Pattern Recognition,2004,37(9):1855-1872.
[8]包磊,徐其志.基于PCA变换和光谱补偿的遥感影像融合方法[J].吉林大学学报:工学版,2013,33(增刊1):88-91.Bao Lei,Xu Qi-zhi.Spectrum-keeping algorithm for fusing based on PCA[J].Journal of Jilin University(Engineering and Technology Edition),2013,33(Supl.1):88-91.
[9]李光鑫,王珂.基于Contourlet变换的彩色图像融合算法[J].电子学报,2007,35(1):112-117.Li Guang-xin,Wang Ke.Color image fusion algorithm Based on contourlet transform[J].Chinese Journal of Electronics,2007,35(1):112-117.
[10]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.
[11]Fu L,Liao Y,Xin L.Image fusion based on nonsubsampled contourlet transform and pulse coupled neural networks[C]?Fourth International Conference on Intelligent Computation Technology and Automation,IEEE Computer Society,2011:572-575.
[12]Ghahremani M,Ghassemian H.Remote-sensing image fusion based on curvelets and ICA[J].International Journal of Remote Sensing,2015,36(16):4131-4143.
[13]Biswas B,Dey A,Dey K N.Remote sensing image fusion using statistical univariate finite mixture model in shearlet domain[C]?International Conference on Advances in Computing,Communications and Informatics.IEEE,2015:2186-2191.
[14]Biswas B,Dey K N,Chakrabarti A.Remote sensing image fusion using multithreshold Otsu method in shearlet domain[J].Procedia Computer Science,2015,57:554-562.
[15]Liu Y,Wang Z.A practical pan-sharpening method with wavelet transform and sparse representation[C]?IEEE International Conference on Imaging Systems and Techniques,IEEE,2014:288-293.
[16]Cheng J,Liu H,Liu T,et al.Remote sensing image fusion via wavelet transform and sparse representation[J].Isprs Journal of Photogrammetry&Remote Sensing,2015,104:158-173.
[17]Metwalli M R,Nasr A H,Allah O S F,et al.Image fusion based on principal component analysis and high-pass filter[C]?International Conference on Computer Engineering&Systems,IEEE,2010:63-70.
[18]Gangkofner U G,Pradhan P S,Holcomb D W.Optimizing the high-Pass filter addition technique for Image fusion[J].Photogrammetric Engineering&Remote Sensing,2008,74(74):1107-1118.
[19]Li W,Hu X,Du J,et al.Adaptive remote-sensing image fusion based on dynamic gradient sparse and average gradient difference[J].International Journal of Remote Sensing,2017,38(23):7316-7332.
[20]Wang Q,Meng Z,Li X.Locality adaptive discriminant analysis for spectral-spatial classification of hyperspectral images[J].IEEE Geoscience&Remote Sensing Letters,2017,14(11):2077-2081.
[21]Gilles J.Empirical wavelet transform[J].IEEETransactions on Signal Processing,2013,61(16):3999-4010.
[22]陈善学,唐义嫄.基于混沌系统的RGB彩色图像三重置乱算法[J].重庆邮电大学学报:自然科学版,2018,30(6):812-818.Chen Shan-xue,Tang Yi-yuan.Triple scrambling algorithm for RGB color image based on chaotic system[J].Chongqing University of Posts and Telecommunications(Natural Science Edition),2018,30(6):812-818.
[23]Gilles J,Tran G,Osher S.2D empirical transforms.wavelets,ridgelets and curvelets revisited[J].Siam Journal on Imaging Sciences,2014,7(7):157-186.
[24]刘磊,张红,王莎.基于小波变换的全局能量图像融合算法[J].吉林大学学报:工学版,2009,39(增刊1):232-236.Liu Lei,Zhang Hong,Wang Sha.Global energy image fusion algorithm based on wavelet transform[J].Journal of Jilin University(Engineering and Technology Edition),2009,39(Supl.1):232-236.