基于非下采样 Contourlet 变换耦合能量相似制约的 遥感图像融合算法
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TP391; TN01

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国家自然科学基金(41574022)、宿迁市科技计划项目(K201914)、江苏省高校自然科学研究面上项目(20KJB170009)资助


Remote sensing image fusion algorithm based on nonsubsampled contourlet transform combined with energy similarity restriction
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    摘要:

    为了克服当前遥感图像融合方法存在的块现象和光谱扭曲等问题,引入了非下采样 Contourlet 变换( nonsubsampled contourlet transform,NSCT),设计了一种通过能量相似制约的遥感图像融合算法。 利用 HSV( hue,saturation,value)变换来处理 多光谱(multi-spectral,MS)图像,从 MS 图像中提取对应的 V 成分,并通过 NSCT 变换计算出 V 成分及全色( panchromatic,PAN) 图像的不同图像系数。 利用区域能量函数来测量图像所含能量信息,并以图像间能量的相似性为依据,构建低频系数的融合模 型,获取具有较好光谱特征的融合低频系数。 利用空间频率函数来测量图像所具有的细节特征,以构建高频系数融合模型,获 取具有较好清晰度的融合高频系数。 最后,借助 HSV 和 NSCT 的逆变换,输出融合结果。 实验结果表明,较现有融合技术而言, 所提算法具有更好的融合效果,输出图像具备更好的光谱特征,有效降低了块效应。

    Abstract:

    In order to overcome the block phenomenon and spectral distortion in the current remote sensing image fusion methods, by introducing the nonsampling contourlet transform, this paper designs a remote sensing image fusion method based on the restriction of energy similarity. HSV transform was applied to multispectral image for extracting its brightness factor from MS image. The different image coefficients of V factor and PAN image were calculated by NSCT transform. The regional energy function was used to measure the image energy information, and the fusion model of low-frequency coefficients was constructed to obtain the fusion low-frequency coefficients with better spectral characteristics. The spatial frequency function was used to measure the detail features of the image, so as to build the fusion model of high-frequency coefficients to obtain the fusion high-frequency coefficients with better definition. Finally, the fusion results were obtained under the inverse transform of HSV and NSCT. The experimental results show that compared with the existing fusion techniques, this algorithm has better fusion effect, and the output image has better spectral characteristics, which effectively reduces the blocking effect.

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余 腾,胡伍生,孙小荣,朱益民.基于非下采样 Contourlet 变换耦合能量相似制约的 遥感图像融合算法[J].电子测量与仪器学报,2021,35(6):71-78

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  • 在线发布日期: 2023-02-27
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