推扫式高光谱谱间压缩感知成像与重构

王忠良; 冯燕; 王丽

doi:10.3788/OPE.20142211.3129

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推扫式高光谱谱间压缩感知成像与重构

信息科学 | 更新时间：2020-08-13

- 推扫式高光谱谱间压缩感知成像与重构
- Compressive sensing imaging and reconstruction of pushbroom hyperspectra
- 光学精密工程 2014年22卷第11期页码：3129-3135
- 作者机构：
  
  1. 西北工业大学电子信息学院,陕西西安,710072
  2. 铜陵学院电气工程系,安徽铜陵,244000
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.61071171)();安徽省高等学校省级自然科学研究基金资助项目(No.KJ2013B298)();西北工业大学博士论文创新基金资助项
- DOI：10.3788/OPE.20142211.3129
  中图分类号： TP75
- 收稿日期：2014-06-25，
  
  修回日期：2014-08-19，
  
  纸质出版日期：2014-11-25
- 稿件说明：
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王忠良, 冯燕, 王丽. 推扫式高光谱谱间压缩感知成像与重构[J]. 光学精密工程, 2014,22(11): 3129-3135

WANG Zhong-liang, FENG Yan, WANG Li. Compressive sensing imaging and reconstruction of pushbroom hyperspectra[J]. Editorial Office of Optics and Precision Engineering, 2014,22(11): 3129-3135
王忠良, 冯燕, 王丽. 推扫式高光谱谱间压缩感知成像与重构[J]. 光学精密工程, 2014,22(11): 3129-3135 DOI： 10.3788/OPE.20142211.3129.

WANG Zhong-liang, FENG Yan, WANG Li. Compressive sensing imaging and reconstruction of pushbroom hyperspectra[J]. Editorial Office of Optics and Precision Engineering, 2014,22(11): 3129-3135 DOI： 10.3788/OPE.20142211.3129.

摘要

提出一种推扫式谱间压缩采样的高光谱成像系统

用于实现高光谱图像的压缩感知成像

并对该系统成像的重构算法进行了研究。在图像采集阶段

采用棱镜对地面成像行的像素进行谱带分离

然后利用数字微镜器件实现谱带的线性编码

通过柱面透镜完成编码谱带的叠加。压缩采样数据重构时

不像传统的压缩感知重构方法那样直接重构高光谱数据

而是利用线性光谱库混合模型将重构高光谱数据转换成重构丰度系数矩阵

采用交替方向乘子法求解丰度的优化问题

再根据重构的丰度和高光谱库恢复原数据。与标准压缩感知重构算法的对比实验表明

该方法在压缩采样数据为总数据的20%时

重构的平均峰值信噪比比标准压缩感知提高了18 dB。所设计的成像系统采样方式简单

可应用于星载或机载的高光谱压缩感知成像。

Abstract

A pushbroom spectral imaging system based on compressive sampling was established to realize compressive sensing imaging for a hyperspectral image. An image reconstruction algorithm for this system was investigated. In the image acquisition stage

the pixels of ground imaging line were separated along spectral direction by a prism. Then

the linear encoding between the spectral bands was realized by a digital micro-mirror device. Finally

the encoded spectral bands were summed by a cylindrical lens. In the reconstruction of the compressive sampled data

the traditional compressive sensing reconstruction methods which recover hyperspectral data directly were abandoned. The liner spectral library mixed models were used to convert the reconstructed hyperspectral data into reconstructed abundance fraction matrix

the alternating direction method of multipliers was used to solve the optimizing problem of abundance

and the data was recovered by using the reconstructed abundance and spectral library. The comparison experiment between standard compressive sensing reconstruction and our algorithm shows that the reconstructed average peak signal noise rate of our algorithm is improved about 18 dB than that of the standard compressive sensing when the used data are 20% that of total data. The system is suitable for the spaceborne airborne hyperspectral compressive sensing imaging for its simple sampling.

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references

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