基于压缩感知的高光谱遥感影像重构方法研究

王晗; 王阿川; 苍圣

doi:10.3788/YJYXS20173203.0219

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基于压缩感知的高光谱遥感影像重构方法研究

显示与图像 | 更新时间：2020-07-07

- 基于压缩感知的高光谱遥感影像重构方法研究
- Hyperspectral remote sensing image reconstruction method based on compressive sensing
- 液晶与显示 2017年32卷第3期页码：219-226
- 作者机构：
  
  东北林业大学信息与计算机工程学院, 黑龙江哈尔滨 150040
- 作者简介：
  
  [ "王晗(1992-),男,吉林长春人,硕士研究生,主要从事高光谱遥感影像处理等方面的研究。E-mail:1wanghan@sina.cn" ]
  王阿川(1964-),男,黑龙江哈尔滨人。教授,博士生导师,主要研究方向：模式识别、计算机视觉、3S技术等。E-mail:wangcal1964@126.com
- 基金信息：
  
  黑龙江省自然科学基金资助项目(No.C201414);哈尔滨市优秀学科带头人基金项目(No.2014R FXXJ040);Supported by Natural Science Foundation of Heilongjiang province(No.C201414);Harbin Good Foundation for Leaders of Disciplines(No.2014RFXXJ040)
- DOI：10.3788/YJYXS20173203.0219
  中图分类号： TP751.1
- 收稿日期：2016-10-26，
  
  录用日期：2016-11-28，
  
  纸质出版日期：2017-03
- 稿件说明：
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王晗, 王阿川, 苍圣. 基于压缩感知的高光谱遥感影像重构方法研究[J]. 液晶与显示, 2017,32(3):219-226.

Han WANG, A-chuan WANG, sheng CANG. Hyperspectral remote sensing image reconstruction method based on compressive sensing[J]. Chinese journal of liquid crystals and displays, 2017, 32(3): 219-226.
王晗, 王阿川, 苍圣. 基于压缩感知的高光谱遥感影像重构方法研究[J]. 液晶与显示, 2017,32(3):219-226. DOI： 10.3788/YJYXS20173203.0219.

Han WANG, A-chuan WANG, sheng CANG. Hyperspectral remote sensing image reconstruction method based on compressive sensing[J]. Chinese journal of liquid crystals and displays, 2017, 32(3): 219-226. DOI： 10.3788/YJYXS20173203.0219.

摘要

高光谱遥感影像包含丰富的空间、辐射以及光谱信息，同时海量的数据也引发了高光谱成像技术在传输和存储方面的诸多问题。针对这一问题，根据高光谱遥感影像谱间相关性强的特性，提出了一种结合谱间多向预测的基于压缩感知的高光谱遥感影像重构方法。首先，根据高光谱遥感影像的谱间相关性对高光谱遥感影像的波段进行分组，每组确定一个参考波段，使用平滑

范数算法重构每组的参考波段。其次，根据重构恢复的相邻组内的参考波段，建立了一个非参考波段预测模型，用来计算非参考波段的预测测量值；然后，计算实际测量值与预测测量值的差值，使用SL0算法重构该差值得到差值向量；最后，利用得到的差值向量迭代更新预测测量值，直到恢复该波段原始图像。仿真实验结果表明，该方法提高了高光谱遥感影像的重构效果。

Abstract

Hyperspectral remote sensing image contains rich space

radiation and spectral information. Meanwhile

massive data also lead to many problems in the transmission and storage of hyperspectral imaging technology. To solve this problem

a new method of hyperspectral remote sensing image reconstruction based on compressive sensing is proposed

which combines the spectral correlation and multi direction prediction. Firstly

according to the spectral correlation of hyperspectral remote sensing image

the bands of hyperspectral remote sensing image are grouped into each group

each group is defined as a reference band

and the I

norm algorithm is used to reconstruct the reference band. Secondly

according to the reference band adjacent reconstructed within the group

a non reference band prediction model is set up

which is used to calculate the predicted measurement non reference band value. Then

we calculate the difference between the actual measured value and the predicted measured values

and reconstruct the difference vector difference using SL0 algorithm. Finally

by using difference vector iteration

we update the prediction measurements until the band recoreries the original image. The simulation results show that the proposed method improves the reconstruction effect of hyperspectral remote sensing image.

关键词

Keywords

references

KEREKES J P, SCHOTT J R. Hyperspectral Imaging Systems [M]//CHANG C I. Hyperspectral Data Exploitation: Theory and Applications. Hoboken, HJ: Wiley-Interscience, 2007: 19-45.

WANG J, CHANG C I. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6): 1586-1600.

赵阳.基于字典学习及压缩感知的高光谱图像重构算法研究[D].秦皇岛:燕山大学,2013.

ZHAO Y.Study on reconstruction of hyperspectral images based on dictionary learning and compressed sensing [D]. Qinhuangdao: Yanshan University, 2013. (in Chinese)

王丽,冯燕.基于空谱联合的多假设预测高光谱图像压缩感知重构算法[J].电子与信息学报,2015,37(12):3000-3008.

WANG L, FENG Y. Compressed sensing reconstruction of hyperspectral images based on spatial-spectral multihypothesis prediction [J]. Journal of Electronics & Information Technology, 2015, 37(12): 3000-3008. (in Chinese)

马馨宏,郭树旭.高光谱遥感图像的稀疏分解与压缩感知[J].吉林大学学报:理学版,2015,53(4):767-772.

MA X H, GUO S X. Compressed sensing and sparse decomposition of hyperspectral remote sensing images [J]. Journal of Jilin University:Science Edition, 2015, 53(4): 767-772. (in Chinese)

贾应彪,冯燕,袁晓玲,等.高光谱图像分块压缩感知采样及谱间预测重构[J].应用科学学报,2014,32(3):281-286.

JIA Y B, FENG Y, YUAN X L, et al. Block compressed sensing sampling and reconstruction using spectral prediction for hyperspectral images [J]. Journal of Applied Sciences, 2014, 32(3): 281-286. (in Chinese)

CHAKRABARTI A, ZICKLER T. Statistics of real-world hyperspectral images [C]//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI: IEEE, 2011: 193-200.

LI W, PRASAD S, FOWLER J E, et al. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1185-1198.

张静.高光谱图像无损及渐进有损压缩编码技术研究[D].西安:西安交通大学,2009.

ZHANG J.Research on hyperspectral imagery lossless and progressive lossy compression technology [D]. Xi'an: Xi'an Jiaotong University, 2009. (in Chinese)

CANDES E J, WAKIN M B. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.

CANDES E, TAO T. Decoding by linear programming [J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.

CANDS E, ROMBERG J K, TAO T. Stable signal recovery from incomplete and inaccurate measurements [J]. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207-1223.

HUO C F, ZHANG R, YIN D. Compression technique for compressed sensing hyperspectral images [J]. International Journal of Remote Sensing, 2012, 33(5): 1586-1604.

MOHIMANI G H, BABAIE-ZADEH M, JUTTEN C. Fast sparse representation based on smoothed 0 norm [C]//Proceedings of the 7th International Conference on Independent Component Analysis and Signal Separation, Berlin Heidelberg: Springer, 2007: 389-396.

MOHIMANI H, BABAIE-ZADEH M, JUTTEN C. A fast approach for overcomplete sparse decomposition based on smoothed l 0 norm [J]. IEEE Transactions on Signal Processing, 2009, 57(1): 289-301.

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