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基于NSST-IHS 变换稀疏表示的SAR 与可见光图像融合

  

  1. 1. 合肥工业大学计算机与信息学院，安徽合肥 230009；2. 工业安全与应急技术安徽省重点实验室，安徽合肥 230009
  • 出版日期:2018-04-30 发布日期:2018-04-30
  • 基金资助:
    国家自然科学基金项目(61371154，41601452)；安徽省重点研究与开发计划项目(1704a0802124)；中国博士后科学基金项目(2016M602005)

Fusion of SAR and Visible Images Based on NSST-IHS and Sparse Representation

  1. 1. School of Computer and Information, Hefei University of Technology, Hefei Anhui 230009, China;
    2. Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei Anhui 230009, China
  • Online:2018-04-30 Published:2018-04-30

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摘要： 针对合成孔径雷达(SAR)与可见光图像成像原理不同，其融合图像常常存在感兴趣
目标不突出及光谱失真的问题，提出了一种基于NSST-IHS 变换稀疏表示的融合算法。对源图
像进行IHS 和NSST 变换，在所得低频分量上采用基于结构相似性和亮度差异性的稀疏表示融
合规则，高频分量上则采用基于改进的拉普拉斯能量和的融合规则，融合结果再通过NSST 和
IHS 逆变换得到。实验以哨兵1 号SAR 图像与landsat-8 可见光图像进行验证，并与传统的IHS、
Wavelet、NSCT、IHS-Wavelet-SR 和NSST-IHS 算法进行比较。结果表明，该算法不论视觉还
是评价指标都有了明显提高，空间结构信息和光谱信息得到有效的保持，有利于后续目标检测
与识别工作。

关键词: 合成孔径雷达图像, 可见光图像, 图像融合, 稀疏表示, 非下采样剪切波变换

Abstract: In order to solve the problem that the interested aims are not prominent and spectral
distortion caused by different imaging mechanism of synthetic aperture radar (SAR) and visible
images, this paper proposes a fusion algorithm based on NSST-IHS and sparse representation. Firstly,
source images are transformed by intensity-hue-saturation (IHS) and non-subsampled shearlet
transform (NSST). Secondly, a fusion rule based on the structure similarity and luminance difference
of the sparse representation is used in low- frequency components, while a fusion rule based on
sum-modified-Laplacian is used in high- frequency components. Finally, the fusion results are
obtained by inverse transformation of NSST and IHS. Experiments are carried out with Sentinel-1A
SAR images and landsat-8 visible images, and compared with the traditional algorithms of IHS,
Wavelet, NSCT, IHS-Wavelet-SR and NSST-IHS. The results show that the new algorithm has
obvious improvement whether in visual or evaluation as well as to maintain the spatial structure
information and spectral information, which is beneficial to target detection and recognition.

Key words: synthetic aperture radar image, visible image, image fusion, sparse representation, non-subsampled shearlet transform