基于分布式压缩感知的遥感图像融合算法

刘静; 李小超; 祝开建; 黄开宇

doi:10.11999/JEIT161393

基于分布式压缩感知的遥感图像融合算法

doi: 10.11999/JEIT161393

1.
(西安交通大学电子与信息工程学院西安 710049) ②(西安卫星测控中心宇航动力学国家重点实验室西安 710043

基金项目:

CAST 创新基金(J20141110)，国家自然科学基金(61573276)，国家 973 计划项目(2013CB329405)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-12-23
- 修回日期: 2017-06-15
- 刊出日期: 2017-10-19

Distributed Compressed Sensing Based Remote Sensing Image Fusion Algorithm

1.
(School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China)

Funds:

The Innovation Foundation of CAST (J20141110), The National Natural Science Foundation of China (61573276), The National 973 Program of China (2013CB329405)

摘要

摘要: 针对基于压缩感知(Compressed Sensing, CS)理论的传统遥感图像融合算法未能考虑源图像信息相关性的特点，该文提出一种基于分布式压缩感知(Distributed Compressed Sensing, DCS)的遥感图像融合改进算法。通过DCS的第1联合稀疏模型(Joint Sparsity Model-1, JSM-1)提取源图像低频信息的公共部分和独有部分，再利用独有特征添加(UFA)的融合规则进行融合，从而提高融合精度。选取QuickBird卫星实测图像数据对该文方法和多个传统融合方法进行仿真实验并进行评价指标的对比，结果表明该文方法融合性能相对传统遥感图像融合方法都有不同程度的提高。
- 遥感图像融合 /
- 分布式压缩感知 /
- 独有特征添加 /
- 信息相关性
Abstract: The conventional Compressed Sensing (CS) based remote sensing image fusion algorithm does not consider the correlation between the source images. In this paper, a novel Distributed CS (DCS) based remote sensing image fusion algorithm is proposed to address the correlation between the source images. The proposed algorithm extracts the common part and the unique part of the low frequency information of the source images, in the framework of Joint Sparsity Model-1 (JSM-1). The Unique Feature Addition (UFA) rule is then used to improve the fusion performance. In the experiments, the QuickBird images are utilized to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the fusion performance is significantly improved using the proposed algorithm, compared with several classical fusion algorithms.
- Remote sensing image fusion /
- Distributed Compressed Sensing (DCS) /
- Unique Feature Addition (UFA) /
- Information correlation

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参考文献(19)

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