利用线性预测与查表法的高光谱图像压缩

宋金伟; 张忠伟; 陈晓敏

doi:10.3788/OPE.20132108.2201

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利用线性预测与查表法的高光谱图像压缩

信息科学 | 更新时间：2020-08-12

- 利用线性预测与查表法的高光谱图像压缩
- Hyperspectral Imagery Compression via Linear Prediction and Lookup Tables
- 光学精密工程 2013年21卷第8期页码：2201-2208
- 作者机构：
  
  中国科学院空间科学与应用研究中心北京,100190
- 作者简介：
- 基金信息：
  
  中国科学院空间科学与应用研究中心&ldquo();五个重点培育方向&rdquo();项目()
- DOI：10.3788/OPE.20132108.2201
  中图分类号： TP751.1
- 收稿日期：2013-01-17，
  
  修回日期：2013-03-25，
  
  网络出版日期：2013-08-20，
  
  纸质出版日期：2013-08-15
- 稿件说明：
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宋金伟张忠伟陈晓敏. 利用线性预测与查表法的高光谱图像压缩[J]. 光学精密工程, 2013,21(8): 2201-2208

SONG Jin-wei ZHANG Zhong-wei CHEN Xiao-min. Hyperspectral Imagery Compression via Linear Prediction and Lookup Tables[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2201-2208
宋金伟张忠伟陈晓敏. 利用线性预测与查表法的高光谱图像压缩[J]. 光学精密工程, 2013,21(8): 2201-2208 DOI： 10.3788/OPE.20132108.2201.

SONG Jin-wei ZHANG Zhong-wei CHEN Xiao-min. Hyperspectral Imagery Compression via Linear Prediction and Lookup Tables[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2201-2208 DOI： 10.3788/OPE.20132108.2201.

摘要

提出了一种线性预测和多谱带查表相结合的高光谱图像无损压缩算法。首先，根据高光谱图像谱带间具有强相关性的特点，建立基于Yule-Walker方程的线性预测模型，其中方程系数矩阵为非Toeplitz形式的对称矩阵，需要使用改进的Levinson算法进行求解。其次，针对校正后的高光谱图像具有稀疏直方图的特点，提出了多谱带查表法，对线性预测的结果进行修正，去除这些图像中因校正引起的信息冗余，而对未校正图像，则不使用该步骤处理。最后，使用熵编码器对预测误差进行编码。分别使用自适应算术编码和Golomb-Rice编码作为熵编码器进行了测试，结果表明：本文算法具有较高的压缩比，压缩效果好于国际空间数据系统咨询委员会（CCSDS）的标准算法。

Abstract

A lossless compression scheme consisting of a linear prediction and multiband lookup tables was proposed to compress the airborne hyperspectral imagery efficiently. Firstly

based on the Yule-Walker equation

a linear prediction model whose equation coefficient matrix is a non-Toeplitz type covariance matrix and it should be solved by an extension form of Levinson algorithm was established by exploiting the strong correlation of spectral bands of hyperspectral imagery. Then

a multiband lookup table algorithm was adopted to refine the prediction result based on the calibrated hyperspectral imagery containing a sparse histogram induced by calibration techniques

however

for the uncalibrated imagery

the multiband lookup tables could be neglected. Finally

the prediction residuals were sent to the entropy encoder. In the experiment

the Adaptive Arithmetic Code and Golomb-Rice Code were both tested as the entropy encoder. The experimental results show that the proposed scheme has a higher compression ratio and the compression effect is better than that of the standard from Consultative Committee for Space Data System（CCSDS）.

关键词

Keywords

references

MIELIKAINEN J. Lossless compression of hyperspectral images using lookup tables [J]. IEEE Signal Processing Letters, 2006, 13(3): 157-160.[2]CCSDS. Lossless multispectral & hyperspectral image compression 123.0-B-1 [S]. CCSDS: CCSDS, 2012.[3]RYAN M J, ARNOLD J F. Lossy compression of hyperspectral data using vector quantization [J]. Remote Sensing Environ., 1997, 61(3): 419-136.[4]WU X L, MEMON N. Context-based lossless interband compression-Extending CALIC [J]. IEEE Transactions on Image Processing, 2000, 9(6): 994-1001.[5]MAGLI E. Multiband lossless compression of hyperspectral images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(4): 1168-1178.[6]AIAZZI B, ALBA P, ALPARONE L, et al.. Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2287-2294.[7]AIAZZI B, ALPARONE L, BARONTI S, et al.. Crisp and fuzzy adaptive spectral predictions for lossless and near-lossless compression of hyperspectral imagery [J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(4): 532-536.[8]MIELIKAINEN J, TOIVANEN P. Clustered DPCM for the lossless compression of hyperspectral images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(12): 2943-2946.[9]汤毅, 辛勤, 李纲, 等. 基于内容的高光谱图像无损压缩 [J]. 光学精密工程, 2012, 20 (3): 668-674.TANG Y, XIN Q, LI G, et al.. Lossless compression of hyperspectral images based on content [J]. Opt. Precision Eng., 2012, 20(3): 668-674. (in Chinese)[10]LIN C C,HWANG Y T. Lossless compression of hyperspectral images using adaptive prediction and backward search schemes [J]. Journal of Information Science and Engineering, 2011, 27(2): 419-435.[11]李进,金旭龙,李国宁.适用于星上应用的高光谱图像无损压缩算法[J]. 光谱学与光谱分析，2012,(33):2264-2269. LI J, JIN X L, LI G N. Lossless compression of hyperspectral image for space-borne application [J]. Spectroscopy and Spectral Analysis, 2012,（33）:2264-2269. (in Chinese)[12]宋娟, 吴成柯, 张静, 等. 基于分类和陪集码的高光谱图像无损压缩 [J]. 电子与信息学报, 2011, 33(1): 231-234. SONG J, WU CH K, ZHANG J, et al.. Lossless compression of hyperspectral images based on classification and coset coding [J]. Journal of Electronics & Information Technology, 2011, 33(1): 231-234. (in Chinese)[13]宋娟, 李云松, 吴成柯, 等. 基于L_最小搜索和陪集码的高光谱图像无损及近无损压缩 [J]. 电子学报, 2011, 39(7):1551-1555. SONG J, LI Y S, WU CH K, et al.. Lossless and near-lossless compression of hyperspectral images based on search for Lminimum and coset coding [J].Act a Electronica Sinica, 2011,39(7) :1551-1555. (in Chinese)[14]张威, 田峰. 超光谱图像分层无损压缩方案 [J]. 小型微型计算机系统, 2011, 32 (12):2499-2503. ZHANG W, TIAN F. Layered lossless compression scheme of hyperspectral image [J]. Journal of Chinese Computer Systems,2011, 32 (12):2499-2503. (in Chinese)[15]张威, 田峰. 机载遥感系统超光谱图像分层近无损压缩 [J]. 计算机科学, 2012,39(7): 293-312. ZHANG W, TIAN F. Layered near-lossless compression scheme of hyperspectral image in airborne remote sensing system [J]. Computer Science.,2012, 39 (7):293-312. (in Chinese)[16]尹传历,李嘉全. 基于位平面的嵌入式超光谱图像压缩系统[J].液晶与显示, 2012, 27(2): 245-249. YIN CH L, LI J Q. Embedded hyperspectral image compression system based on bit-plane [J]. Chinese Journal of Liquid Crystals and Displays, 2012, 27(2):245-249. (in Chinese)[17]万建伟, 粘永健, 苏令华, 等. 高光谱图像压缩技术研究进展 [J]. 信号处理, 2010, 26(9):1397-1407. WAN J W, NIAN Y J, SU L H, et al.. Research progress on hyperspectral imagery compression technique [J]. Signal Processing,2010,26(9):1397-1407. (in Chinese)[18]粘永健, 辛勤, 汤毅, 等. 基于多波段预测的高光谱图像分布式无损压缩 [J]. 光学精密工程, 2012,20(4): 906-912.NIAN Y J, XIN Q,TANG Y, et al.. Distributed lossless compression of hyperspectral images based on multi-band prediction [J] . Opt. Precision Eng.,2012,20(4):906-912. (in Chinese)[19]CIZNICKI M, KUROWSKI K, PLAZA A. Graphics processing unit implementation of JPEG2000 for hyperspectral image compression [EB/OL]. (2012-06) http://spiedigitallibrary.org/ on 12/11/2012 Terms of Use: http://spiedl.org/terms.[20]KIELY A B, KLIMESH M A. Exploiting calibration-induced artifacts in lossless compression of hyperspectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(8):2672-2678.[21]SANCHEZ J E, AUGE E, SANTALO J, et al.. Review and implementation of the emerging CCSDS recommended standard for multispectral and hyperspectral lossless image coding [C]. 2011 First International Conference on Data Compression, Communications and Processing, 2011.[22]MIELIKAINEN J, TOIVANEN P. Lossless compression of hyperspectral images using a quantized index to lookup tables [J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(3): 474-478.[23]LIN C C,HWANG Y T. An efficient lossless compression scheme for hyperspectral images using two-stage prediction [J]. IEEE Geoscience and Remote Sensing Letters, 2011, 7(3): 558-562.[24]PORSANI M, ULRYCH T J. Levinson type extensions for non toeplitz systems[J]. IEEE Transactions on Signal Processing, 1991, 39(2): 366-375.

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