Lossless compression of hyperspectral images based on contents

TANG Yi; XIN Qin; LI Gang; WAN Jian-wei

doi:10.3788/OPE.20122003.0668

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Lossless compression of hyperspectral images based on contents

Article | 更新时间：2020-08-12

- Lossless compression of hyperspectral images based on contents
- Optics and Precision Engineering Vol. 20, Issue 3, Pages: 668-674(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.20122003.0668
  CLC： TH751.1;TP391.4
- Received：14 September 2011，
  
  Revised：31 October 2011，
  
  Published Online：22 March 2012，
  
  Published：22 March 2012
- 稿件说明：
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汤毅, 辛勤, 李纲, 万建伟. 基于内容的高光谱图像无损压缩[J]. 光学精密工程, 2012,(3): 668-674

TANG Yi, XIN Qin, LI Gang, WAN Jian-wei. Lossless compression of hyperspectral images based on contents[J]. Editorial Office of Optics and Precision Engineering, 2012,(3): 668-674
汤毅, 辛勤, 李纲, 万建伟. 基于内容的高光谱图像无损压缩[J]. 光学精密工程, 2012,(3): 668-674 DOI： 10.3788/OPE.20122003.0668.

TANG Yi, XIN Qin, LI Gang, WAN Jian-wei. Lossless compression of hyperspectral images based on contents[J]. Editorial Office of Optics and Precision Engineering, 2012,(3): 668-674 DOI： 10.3788/OPE.20122003.0668.

摘要

提出了一种基于内容的高光谱图像无损压缩算法。采用自适应波段选择算法对高光谱图像进行降维

引入C-means算法对降维后的光谱矢量进行无监督分类。利用单调后向排序算法确定波段的预测顺序

并根据相邻波段的相关系数大小进行自适应波段分组。针对每一类地物

选取类内部分像素进行最优预测系数的训练

采用多波段线性预测的方案去除同类像素的谱间相关性

预测残差进行JPEG-LS无损压缩。对机载可见光/红外成像光谱仪(AVIRIS)与实用型模块化成像光谱仪(OMIS)获取的高光谱图像分别进行实验

并与未进行分类预测的算法比较。结果显示

提出的算法的平均压缩比分别提高约0.11和0.7

验证了该算法在无损压缩方面的有效性。

Abstract

A lossless compression algorithm based on contents was proposed for hyperspectral images. An adaptive band selection algorithm was introduced to reduce the dimensionality of hyperspectral images

and a C-means algorithm was used to classify the spectral vectors resulting from dimensionality reduction unsupervisedly. Then

the reverse monotonic ordering method was taken to determine the prediction ordering

hyperspectral images were divided into groups adaptively according to the correlation between each adjacent bands

and the scheme of multi-band linear prediction was used to eliminate the spectral redundancy of the identical class. For each class

partial pixels within this class were selected to train optimal predictive coefficients

and predictive errors were compressed in lossless by JPEG-LS standard. Experiments were performed for the hyperspectral images acquired by an Airborne Visible/Infrared Imaging Spectrometer(AVIRIS) and an Operational Modular Imaging Spectrometer(OMIS). Experiental results show that the average compression ratio of the proposed algorithm can be improved about 0.11 and 0.7 respectively as compared with above algorithms without classification prediction.

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references

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