基于空谱联合和波段分类的高光谱压缩感知重构

黄元超; 王阿川

doi:10.3788/YJYXS20183304.0291

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基于空谱联合和波段分类的高光谱压缩感知重构

图像处理 | 更新时间：2020-07-06

- 基于空谱联合和波段分类的高光谱压缩感知重构
- Hyperspectral compressed perceptual reconstruction based on space spectrum combination and band classification
- 液晶与显示 2018年33卷第4期页码：291-298
- 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：
  
  [ "黄元超(1993-), 男, 山西运城人, 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:HyuanC9324@163.com" ]
  [ "王阿川(1964-), 男, 黑龙江哈尔滨人, 教授, 硕士生导师, 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(C201414);哈尔滨市优秀学科带头人基金项目(2014R FXXJ040);the Natural Science Foundation of Heilongjiang Province(C201414);Harbin Foundation for Good Leaders of Disciplines(2014RFXXJ040)
- DOI：10.3788/YJYXS20183304.0291
  中图分类号： TP751.1
- 收稿日期：2017-11-16，
  
  录用日期：2017-12-17，
  
  纸质出版日期：2018-04-05
- 稿件说明：
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黄元超, 王阿川. 基于空谱联合和波段分类的高光谱压缩感知重构[J]. 液晶与显示, 2018,33(4):291-298.

Yuan-chao HUANG, A-chuan WANG. Hyperspectral compressed perceptual reconstruction based on space spectrum combination and band classification[J]. Chinese journal of liquid crystals and displays, 2018, 33(4): 291-298.
黄元超, 王阿川. 基于空谱联合和波段分类的高光谱压缩感知重构[J]. 液晶与显示, 2018,33(4):291-298. DOI： 10.3788/YJYXS20183304.0291.

Yuan-chao HUANG, A-chuan WANG. Hyperspectral compressed perceptual reconstruction based on space spectrum combination and band classification[J]. Chinese journal of liquid crystals and displays, 2018, 33(4): 291-298. DOI： 10.3788/YJYXS20183304.0291.

摘要

卫星在获取地面信息时会受到大气、电磁波的干扰，导致高光谱影像本身产生坏线和噪声。针对这一问题，本文结合高光谱遥感影像的特性提出了一种基于空谱联合和波段分类的影像重构方法。首先，根据噪声影响程度将影像波段分为坏线强干扰波段和非干扰低噪声波段；其次，对波段进行分组，确定每组参考波段，并对参考波段进行独立重构；然后，根据参考波段构建双模式空谱联合预测模型，利用正则化交叉投影得到非参考波段重构影像；最后，对坏线强干扰波段，先进行独立重构，然后对重构影像进行小波分解，通过高频校正得到了干扰波段最终重构影像。实验表明，本文方法对重构高光谱影像的平均信噪比较传统方法提高了1~2 dB。

Abstract

Satellites are subject to the interference of the atmosphere and electromagnetic wave when obtaining ground information

resulting in the occurrence of bad lines and noise in the hyperspectral image itself. In response to this problem

this paper

taking the characteristics of hyperspectral remote sensing images into account

presents an image reconstruction method based on the combination of hyperspectral and band classification. Firstly

according to the level of noise

the image band is classified as two levels:strong interference band due to bad line and the non-interference low noise band. Secondly

the second level is divided into groups

and then the standard band is determined. The standard band is independently reconstructed and the dual-mode space spectrum joint prediction model is constructed referring to the standard band. The reconstruction image of non-reference band is obtained by cross-projection using the regularization theory. Finally

the strong interference band is reconstructed independently and the reconstructed image is decomposed by wavelet transform

leading to the final image of the interference band by high-frequency correction. Experimental results show that the proposed method improves the average signal-to-noise ratio of the reconstructed image by about 1~2 dB compared with the traditional one.

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references

YUL, WEI C, JIA J Y, et al.Compressive sensing for cluster structured sparse signals:variational Bayes approach[J]. IET Signal Processing, 2016, 10(7):770-779.

SAABR, WANG R R, YILMAZ Ö. From compressed sensing to compressed bit-streams:practical encoders, tractable decoders[J]. IEEE Transactions on Information Theory, 2016, 99:1-1. (in press)

柯洪昌, 孙宏彬.图像序列的显著性目标区域检测方法[J].中国光学, 2015, 8(5):768-774.

KE H C, SUN H B. A saliency target area detection method of image sequence[J]. Chinese Optics, 2015, 8(5):768-774. (in Chinese)

VEHKAPERÄ M, KABASHIMA Y, CHATTERJEE S. Analysis of regularized LS reconstruction and random matrix ensembles in compressed sensing[J]. IEEE Transactions on Information Theory, 2016, 62(4):2100-2124.

陈燕芹, 段锦, 祝勇, 等.基于纹理特征的图像复杂度研究[J].中国光学, 2015, 8(3):407-414.

CHEN Y Q, DUAN J, ZHU Y, et al. Research on the image complexity based on texture features[J].Chinese Optics, 2015, 8(3):407-414. (in Chinese)

DAI W, MILENKOVIC O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5):2230-2249.

李尊, 苗同军.基于DWT-IRLS的压缩感知图像融合[J].液晶与显示, 2017, 32(1):35-39.

LI Z, MIAO T J. Image fusion based on CS of DWT-IRLS[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(1):35-39. (in Chinese)

WANG B C, DAI L L, ZHANG Y, et al. Dynamic compressive sensing-based multi-user detection for uplink grant-free NOMA[J]. IEEE Communications Letters, 2016, 20(11):2320-2323.

REEVES G, PFISTER H D. The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact[C]//Proceedings of IEEE International Symposium on Information Theory. Barcelona, Spain: IEEE, 2016: 665-669.

GIRYES R, ELAD M. RIP-based near-oracle performance guarantees for SP, CoSaMP, and IHT[J]. IEEE Transactions on Signal Processing, 2012, 60(3):1465-1468.

XUC, HUANG D Q, HAN W. Hyperspectral image compression and reconstruction based on compressed sensing[J]. International Journal of Multimedia and Ubiquitous Engineering, 2015, 10(2):351-360.

CASTROR M, TÁNCZOS E. Adaptive compressed sensing for support recovery of structured sparse sets[J]. IEEE Transactions on Information Theory, 2017, 63(3):1535-1554.

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基于压缩感知的高光谱遥感影像重构方法研究