Distributed lossless compression of hyperspectral images based on multi-band prediction

NIAN Yong-jian; XIN Qin; TANG Yi; WAN Jian-wei

doi:10.3788/OPE.20122004.0906

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Distributed lossless compression of hyperspectral images based on multi-band prediction

Article | 更新时间：2020-08-12

- Distributed lossless compression of hyperspectral images based on multi-band prediction
- Optics and Precision Engineering Vol. 20, Issue 4, Pages: 906-912(2012)
- 作者机构：
  
  国防科技大学电子科学与工程学院, 湖南长沙 410073
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122004.0906
  CLC： TP751.1
- Received：23 November 2011，
  
  Revised：05 January 2012，
  
  Published Online：22 April 2012，
  
  Published：22 April 2012
- 稿件说明：
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粘永健, 辛勤, 汤毅, 万建伟. 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,(4): 906-912

NIAN Yong-jian, XIN Qin, TANG Yi, WAN Jian-wei. Distributed lossless compression of hyperspectral images based on multi-band prediction[J]. Editorial Office of Optics and Precision Engineering, 2012,(4): 906-912
粘永健, 辛勤, 汤毅, 万建伟. 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,(4): 906-912 DOI： 10.3788/OPE.20122004.0906.

NIAN Yong-jian, XIN Qin, TANG Yi, WAN Jian-wei. Distributed lossless compression of hyperspectral images based on multi-band prediction[J]. Editorial Office of Optics and Precision Engineering, 2012,(4): 906-912 DOI： 10.3788/OPE.20122004.0906.

摘要

提出了一种基于分布式信源编码的高光谱图像无损压缩算法

用于星载高光谱数据的有效压缩。为充分利用高光谱图像较强的谱间相关性

引入多波段谱间线性预测方案获取当前编码块的预测值

有效降低了编码块的最大预测残差值。在此基础上

根据最大预测残差值确定编码块各像素所属陪集的索引

通过传输每个像素所属陪集的索引代替预测残差

实现高光谱图像压缩。对星载可见/红外成像光谱仪(AVIRIS)获取的高光谱图像进行实验

并与已有的典型算法进行比较

结果显示该算法能够取得较好的无损压缩效果

同时具有较低的编码复杂度

适用于星载高光谱图像的无损压缩。

Abstract

A lossless compression algorithm based on distributed source coding was proposed to compress the airborne hyperspectral data effectively. In order to make full use of the spectral correlation of hyperspectral images

a multi-band prediction scheme was introduced to acquire the prediction values of the current block and to reduce the maximal absolute value of prediction error effectively. Then

by using the maximal absolute value to determine the coset index of pixels belonging to the current block

the lossless compression of hyperspectral images was realized by transmitting the coset index of the current block instead of its prediction error. Experimental results on hyperspectral images acquired by Airborne Visible Infrared Imaging Spectrometer (AVIRIS) show that the proposed algorithm can offer both high compression performance and low encoder complexity compared with those existing classical algorithms

which is available for on-board compression of hyperspectral images.

关键词

Keywords

references

MAGLI E. Multiband lossless compression of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(4):1168-1178.[2] HUO C F, ZHANG R, PENG T X. Lossless compression of hyperspectral images based on searching optimal multibands for prediction[J]. IEEE Geoscience and Remote Sensing Letters, 2009,6(2):339-343.[3] LIN C C,WANG Y T. An efficient lossless compression scheme for hyperspectral images using two-stage prediction[J]. IEEE Geoscience and Remote Sensing Letters, 2010,7(3):558-562.[4] ZHANG J, LIU G Z. An efficient reordering prediction-based lossless compression algorithm for hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2007,4(2):283-287.[5] 高恒振,万建伟,粘永健,等. 组合核函数支持向量机高光谱图像融合分类[J]. 光学精密工程,2011,19(4):878-883. GAO H ZH, WAN J W, NIAN Y J, et al.. Fusion classification of hyperspectral image by composite kernels support vector machine[J]. Opt. Precision Eng., 2011,19(4):878-883. (in Chinese)[6] 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.[7] ABRARDO A, BARNI M, MAGLI E, et al.. Error-resilient and low-complexity onboard lossless compression of hyperspectral images by means of distributed source coding[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(4):1892-1904.[8] 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. Lossless compression of hyperspectral images based on classification and coset coding[J]. Journal of Electronics&Information Technology, 2011,33(1):231-234. (in Chinese)[9] SLEPIAN D, WOLF J K. Noiseless coding of correlated information sources[J]. IEEE Transactions on Information Theory, 1973,19(4):471-480.[10] MAGLI E, BARNI M, ABRARDO A, et al.. Distributed source coding techniques for lossless compression of hyperspectral images[J]. EURASIP Journal on Advanced Signal Processing, 2007, 2007(1):1-13.[11] ROGER R, CAVENOR C. Lossless compression of AVIRIS images[J]. IEEE Transaction on Image Processing, 1996,5(5):713-719.[12] RIZZO F, CARPENTIERI B, MOTTA G, et al.. Low-complexity lossless compression of hyperspectral imagery via linear prediction[J]. IEEE Signal Processing Letter, 2005,12(2):138-141.[13] WU X L, MEMON N. Context-based lossless interband compression extending CALIC[J]. IEEE Transaction on Image Processing, 2000,9(6):994-1001. [14] RIZZO F, CARPENTIERI B. High performance compression of hyperspectral imagery width reduced search complexity in the compressed domain. Proceedings Data Compression Conference, 2004:479-488.

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