基于双正交小波变换耦合区域梯度特征的遥感图像融合算法
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摘要
当前较多遥感图像融合算法是利用主成分分析方法来完成遥感图像的融合,由于主成分分析方法融合后的图像会产生光谱畸变,易导致所融合图像存在光谱失真的问题。对此,设计了一种采用双正交小波变换耦合区域梯度特征的遥感图像融合算法。对多光谱图像进行色调-饱和度-亮度变换,以获取多光谱图像的亮度分量,引入双正交小波变换将该亮度分量与全色图像进行小波域分解,以获取图像的低频与高频子带;通过低频子带中像素点的区域梯度特征构造均值梯度模型,用于求取低频子带融合系数,利用高频子带中像素点对应的区域方差构造相似度因子,用于求取高频子带融合系数;通过色调-饱和度-亮度与双正交小波的逆变换获取所融合遥感图像。仿真实验结果显示,所设计方法与当前遥感图像融合方法相比,融合的遥感图像具有更好的视觉效果。
The current many remote sensing image fusion algorithms use orthogonal filter to obtain the high frequency and low frequency bands of the image and then complete the fusion of remote sensing images.Because the orthogonal filter can produce the phase distortion,it can easily lead to the defects of the fused image,such as the edge distortion.Therefore,a remote sensing image fusion algorithm based on biorthogonal wavelet transform coupled with regional gradient features is designed.First,the HSV transform of multispectral images is used to get the luminance components of multispectral images.The biorthogonal wavelet transform is introduced to decompose the luminance component and panchromatic image in the wavelet domain,so as to get the low frequency and high frequency sub-band of the image.Then,the mean gradient model is constructed through the gradient characteristics of the pixels in the low frequency sub-band,which is used to obtain the low frequency sub-band fusion coefficient.The similarity factor is constructed by using the region variance corresponding to pixels in the high frequency sub-band,which is used to obtain the high frequency sub-band fusion coefficient.Finally,the fused remote sensing image is got by inverse transformation of HSV and biorthogonal wavelet.The simulation results show that the proposed method has better visual effect compared with the current remote sensing image fusion method.
The current many remote sensing image fusion algorithms use orthogonal filter to obtain the high frequency and low frequency bands of the image and then complete the fusion of remote sensing images.Because the orthogonal filter can produce the phase distortion,it can easily lead to the defects of the fused image,such as the edge distortion.Therefore,a remote sensing image fusion algorithm based on biorthogonal wavelet transform coupled with regional gradient features is designed.First,the HSV transform of multispectral images is used to get the luminance components of multispectral images.The biorthogonal wavelet transform is introduced to decompose the luminance component and panchromatic image in the wavelet domain,so as to get the low frequency and high frequency sub-band of the image.Then,the mean gradient model is constructed through the gradient characteristics of the pixels in the low frequency sub-band,which is used to obtain the low frequency sub-band fusion coefficient.The similarity factor is constructed by using the region variance corresponding to pixels in the high frequency sub-band,which is used to obtain the high frequency sub-band fusion coefficient.Finally,the fused remote sensing image is got by inverse transformation of HSV and biorthogonal wavelet.The simulation results show that the proposed method has better visual effect compared with the current remote sensing image fusion method.
引文
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[20]纪峰,李泽仁,常霞.基于PCA和NSCT变换的遥感图像融合方法[J].图学学报,2017,38(2):247-252.Ji Feng,Li Zeren,Chang Xia.Remote sensing image fusion method based on PCA and NSCT transform[J].Journal of Graphics,2017,38(2):247-252.
[21]Zhang Libao,Zhang Jue.A New saliency-driven fusion method based on complex wavelet transform for remote sensing images[J].IEEE Geoscience and Remote Sensing Letters,2017,14(12):2433-2437.
[22]Wei Jingbo,Wang Lizhe,Liu Peng.Spatiotemporal fusion of remote sensing images with structural sparsity and semi-coupled dictionary learning[J].Remote Sensing,2017,9(1):21-29.
[23]李旭,高雅楠,Shigang Yue.一种尺度感知型遥感图像融合新方法[J].宇航学报,2017,38(12):1348-1353.Li Xu,Gao Yanan,Yue Shigang.A novel scale-aware pansharpening method[J].Journal of Astronautics,2017,38(12):1348-1353.
[2]Khaled Hazaymeh,Quazi K.Hassan.A remote sensing-based agricultural drought indicator and its implementation over a semi-arid region,Jordan[J].Journal of Arid Land,2017,9(3):319-330.
[3]Wu Jianglong,Tian Xiaolin.Remote image fusion for mars data using mix of robust PCA[J].International Journal of Pattern Recognition and Artificial Intelligence,2017,31(1):1-15.
[4]王威,张佳娥.引导滤波和稀疏表示相结合的遥感图像融合算法[J].小型微型计算机系统,2017,38(3):601-604.Wang Wei,Zhang Jiae.Remote Sensing image fusion method based on guided filter and sparse representation[J].Journal of Chinese Computer Systems,2017,38(3):601-604.
[5]Wang Jun,Peng Jinye,Jiang Xiaoyue.Remote-sensing image fusion using sparse representation with sub-dictionaries[J].International Journal of Remote Sensing,2017,38(12):3564-3585.
[6]李红,王伟,吴粉侠.基于Contourlet变换和广义高斯参数估计的遥感图像融合[J].咸阳师范学院学报,2017,32(6):43-47.Li Hong,Wang Wei,Wu Fenxia.Pansharpening with CT and generalized gaussian distribution[J].Journal of Xianyang Normal University,2017,32(6):43-47.
[7]戴洪宝,许继影,夏青森.基于IHS变换和小波变换的遥感影像融合[J].安阳师范学院学报,2016,5(1):92-95.Dai Hongbao,Xu Jiying,Xia Qingsen.Remote sensing image based on ihs transform and wavelet transform fusion[J].Journal of Anyang Normal University,2016,5(1):92-95.
[8]Sulochana S,Vidhya R,Manonmani R.Optical image fusion using support value transform(SVT)and curvelets[J].Optik-International Journal for Light and Electron Optics,2015,126(18):1672-1675.
[9]都伟冰,谢玉娟,杨文.结合HSV与PCA变换的遥感影像融合[J].江苏科技信息,2017,11(4):44-46.Du Weibing,Xie Yujuan,Yang Wen.Study on remote sensing image fusion that combines changes between HSV and PCA[J].Jiangsu Science&Technology Information,201711(4):44-46.
[10]Wang Wenhao,Sun Shanlin,Jiang Mingxin.Traffic lights detection and recognition based on multi-feature fusion[J].Multimedia Tools and Applications,2017,76(13):14829-14846.
[11]古丽米热·米吉提,吐尔洪江·阿布都克力木.基于二进小波变换的遥感图像融合方法[J].新疆师范大学学报:自然科学版,2017,36(4):83-85.Gulimure·Mijiti,Turghunjan·Abdukirim.Remote sensing image fusion method based on dyadic wavelet transform[J].Journal of Xinjiang Normal University(Natural Sciences Edition),2017,36(4):83-85.
[12]Chen Zhiyu,Muramatsu Shogo.Multi-Focus image fusion based on multiple directional LOTs[J].Jeice transactions on Fundamentals of Electronics,Communications and Computer Sciences,2015,E98-A(11):2360-2365.
[13]Manish Sharmab,Abhinav Dherec,Ram Bilas Pachorib.Optimal duration-bandwidth localized antisymmetric biorthogonal wavelet filters[J].Signal Processing,2017,134(5):87-99.
[14]Manish Sharma,Achuth P V,Ram Bilas Pachori.A parametrization technique to design joint time-frequency optimized discrete-time biorthogonal wavelet bases[J].Signal Processing,2017,135(6):107-120.
[15]Zhao Jufeng,Cui Guangmang,Gong Xiaoli.Fusion of visible and infrared images using global entropy and gradient constrained regularization[J].Infrared Physics and Technology,2017,81(3):201-209.
[16]Yang Yong,Que Yue,Huang Shuying.Multiple visual features measurement with gradient domain guided filtering for multisensor image fusion[J].IEEE Transactions on Instrumentation&Measurement,2017,66(4):691-703.
[17]Wang Juan,Lai Siyu,Borjer T H.Image fusion based on WRVand NHM rules for medical digital image in contourlet domain[J].Journal of Digital Information Management,2016,14(3):151-159.
[18]Bao Wenxing,Wang Wei,Zhu Yunxiao.Pleiades satellite remote sensing image fusion algorithm based on shearlet transform[J].Journal of the Indian Society of Remote Sensing,2018,46(1):19-29.
[19]Liu Xuan,Zhou Yue,Wang Jiajun.Image fusion based on shearlet transform and regional features[J].Aeu:International Journal of Electronics and Communications,2014,68(6):471-477.
[20]纪峰,李泽仁,常霞.基于PCA和NSCT变换的遥感图像融合方法[J].图学学报,2017,38(2):247-252.Ji Feng,Li Zeren,Chang Xia.Remote sensing image fusion method based on PCA and NSCT transform[J].Journal of Graphics,2017,38(2):247-252.
[21]Zhang Libao,Zhang Jue.A New saliency-driven fusion method based on complex wavelet transform for remote sensing images[J].IEEE Geoscience and Remote Sensing Letters,2017,14(12):2433-2437.
[22]Wei Jingbo,Wang Lizhe,Liu Peng.Spatiotemporal fusion of remote sensing images with structural sparsity and semi-coupled dictionary learning[J].Remote Sensing,2017,9(1):21-29.
[23]李旭,高雅楠,Shigang Yue.一种尺度感知型遥感图像融合新方法[J].宇航学报,2017,38(12):1348-1353.Li Xu,Gao Yanan,Yue Shigang.A novel scale-aware pansharpening method[J].Journal of Astronautics,2017,38(12):1348-1353.