Hyperspectral image lossless compressionusing adaptive bands selection and optimal prediction sequence

Fu-quan ZHU; Li-ping YANG; Chang-guo LI

doi:10.37188/OPE.20202807.1609

您当前的位置：

首页 >

文章列表页 >

Hyperspectral image lossless compressionusing adaptive bands selection and optimal prediction sequence

Information Sciences | 更新时间：2020-08-13

- Hyperspectral image lossless compressionusing adaptive bands selection and optimal prediction sequence
- Optics and Precision Engineering Vol. 28, Issue 7, Pages: 1609-1617(2020)
- 作者机构：
  
  1.成都理工大学地球物理学院, 四川成都 610059
  2.四川警察学院教务处, 四川泸州 646000
  3.四川警察学院基础教学部, 四川泸州 646000
  4.四川师范大学基础教学学院, 四川成都 610068
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20202807.1609
  CLC： TP751.1
- Received：19 January 2020，
  
  Revised：01 March 2020，
  
  Accepted：01 March 2020，
  
  Published：15 July 2020
- 稿件说明：
移动端阅览
Fu-quan ZHU, Li-ping YANG, Chang-guo LI. Hyperspectral image lossless compressionusing adaptive bands selection and optimal prediction sequence[J]. Optics and precision engineering, 2020, 28(7): 1609-1617.
DOI：

Fu-quan ZHU, Li-ping YANG, Chang-guo LI. Hyperspectral image lossless compressionusing adaptive bands selection and optimal prediction sequence[J]. Optics and precision engineering, 2020, 28(7): 1609-1617. DOI： 10.37188/OPE.20202807.1609.

摘要

针对传统递归最小二乘预测器的预测精度与谱间相关程度存在较强相关性及其对预测顺序较为敏感的特点，提出一种基于自适应波段选择和最佳预测顺序的高光谱图像无损压缩方法。首先，为了提高参考波段与待预测波段间的谱间相关性，以最大谱间相关系数为准则进行波段重排预处理，接着引入自适应波段选择策略从已预测波段集中选出与待预测波段存在最高相关性的多个波段作为参考波段。然后，以最小预测残差熵为准则选出最佳预测顺序模式进行谱间预测。最后，采用算术编码器对预测残差进行熵编码。在AVIRIS 2006数据集上的实验结果显示，该方法在16位校正图像、16位未校正图像和12位未校正图像上分别取得了3.314，5.594和2.395 bpp的压缩效果。该方法在几乎不增加计算复杂度的情况下有效提高了传统递归最小二乘预测器的预测精度，其最佳压缩效果接近或优于其他同类方法。

Abstract

The prediction accuracy of a Conventional Recursive Least Square (CRLS) predictor is strongly correlated with the inter-spectral correlation and is sensitive to the sequence in which the pixels are predicted. In view thereof

a lossless compression method for hyperspectral images was proposed. The method

which was based on the CRLS predictor

was modified to enable the selection of adaptive bands and to optimize the prediction sequence mode. First

to improve the correlation between the reference bands and the band to be predicted

the bands of the hyperspectral image were reordered according to the criterion of the maximum inter-spectral correlation coefficient in the preprocessing stage. Subsequently

the adaptive band selection strategy was used to select multiple bands with the highest correlation with the band to be predicted for use as prediction reference bands. Afterwards

the CRLS predictor with the best prediction sequence mode

selected by the minimum prediction residual entropy

was used for inter-spectral prediction. Finally

the arithmetic encoder was used to encode the prediction residual. Experiments on the AVIRIS 2006 dataset show that this method achieves bit rates of 3.314

5.594

and 2.395 bpp on a 16-bit calibrated image

16-bit uncalibrated image

and 12-bit uncalibrated image

respectively. These results indicate that this method can effectively improve the prediction accuracy of the CRLS predictor without significantly increasing the computational complexity. The best result of the proposed method closely approximates or is superior to that of other similar methods.

关键词

Keywords

references

闫敬文 , 陈宏达 , 刘蕾 . 高光谱图像分类的研究进展 . 光学精密工程 , 2019 . 27 ( 3 ): 680 - 693 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201903020 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201903020 .

J W YAN , H D CHEN , L LIU . Overview of hyperspectral image classification . Opt. Precision Eng. , 2019 . 27 ( 3 ): 680 - 693 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201903020 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201903020 .

王含宇 , 杨名宇 , 王浩 , 等 . 融合微分重排与匹配的高光谱快速目标检测 . 液晶与显示 , 2019 . 34 ( 8 ): 793 - 802 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yjyxs201908010 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yjyxs201908010 .

H Y WANG , M Y YANG , H WANG , 等 . A fast hyperspectral target detection fused on spectral derivative and recomposition and SAM . Chinese Journal of Liquid Crystals and Displays , 2019 . 34 ( 8 ): 793 - 802 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yjyxs201908010 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yjyxs201908010 .

张淳民 , 穆廷魁 , 颜廷昱 , 等 . 高光谱遥感技术发展与展望 . 航天返回与遥感 , 2018 . 39 ( 3 ): 104 - 114 . DOI: 10.3969/j.issn.1009-8518.2018.03.012 http://doi.org/10.3969/j.issn.1009-8518.2018.03.012 .

CH M ZHANG , T K MU , T Y YAN , 等 . Overview of hyperspectral remote sensing Technology . Spacecraft Recovery & Remote Sensing , 2018 . 39 ( 3 ): 104 - 114 . DOI: 10.3969/j.issn.1009-8518.2018.03.012 http://doi.org/10.3969/j.issn.1009-8518.2018.03.012 .

W FU , SH T LI , L Y FANG , 等 . Adaptive spectral-spatial compression of hyperspectral image with sparse representation . IEEE Transactions on Geoscience and Remote Sensing , 2017 . 55 ( 2 ): 671 - 682 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cb01c2680a5f98b0bfcc9f87f4faac1e http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cb01c2680a5f98b0bfcc9f87f4faac1e .

R GUERRA , Y BARRIOS , M DIAZ , 等 . A new algorithm for the on-board compression of hyperspectral images . Remote Sensing , 2018 . 10 428 DOI: 10.3390/rs10030428 http://doi.org/10.3390/rs10030428 .

D BASCONES , C GONZALEZ , D MOZOS . Hyperspectral image compression using vector quantization, PCA and JPEG2000 . Remote Sensing , 2018 . 10 ( 6 ): 907 DOI: 10.3390/rs10060907 http://doi.org/10.3390/rs10060907 .

陈善学 , 张燕琪 . 基于自适应波段聚类主成分分析和反向传播神经网络的高光谱图像压缩 . 电子与信息学报 , 2018 . 40 ( 10 ): 2478 - 2483 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkxxk201810027 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkxxk201810027 .

SH X CHEN , Y Q ZHANG . Hyperspectral image compression based on adaptive band clustering principal component analysis and back propagation neural network . Journal of Electronics & Information Technology , 2018 . 40 ( 10 ): 2478 - 2483 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkxxk201810027 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkxxk201810027 .

H CHEN , B HUANG , Y ZHANG . Adaptive quantized PCA with 3D prediction and positive packing for lossless compression of ultraspectral sounder data . International Journal of Remote Sensing , 2015 . 36 ( 6 ): 1689 - 1704 . DOI: 10.1080/01431161.2015.1017665 http://doi.org/10.1080/01431161.2015.1017665 .

HUANG B, AHUJA A, HUANG H L. Fast precomputed VQ with optimal bit allocation for lossless compression of ultraspectral sounder data[C]. Data Compression Conference, Snowbird, UT, USA, 2005: 29-31.

E MAGLI , G OLMO , E QUACCHIO . Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC . IEEE Geoscience and Remote Sensing Letters , 2004 . 1 ( 1 ): 21 - 25 . DOI: 10.1109/LGRS.2003.822312 http://doi.org/10.1109/LGRS.2003.822312 .

M WEINBERGER , G SEROUSSI , G SAPIRO . The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS . IEEE Transacions on Image Processing , 2000 . 9 ( 8 ): 1309 - 1324 . DOI: 10.1109/83.855427 http://doi.org/10.1109/83.855427 .

B AIAZZI , S BARONTI , L ALPARONE . Lossless compression of hyperspectral images using multiband lookup tables . IEEE Signal Processing Letters , 2009 . 16 ( 6 ): 481 - 484 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0ec6a27c61335a61c5b91b1488f8b3a5 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0ec6a27c61335a61c5b91b1488f8b3a5 .

J MIELIKAINEN , B HUANG . Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length . IEEE Geoscience and Remote Sensing Letters , 2012 . 9 ( 6 ): 1118 - 1121 . DOI: 10.1109/LGRS.2012.2191531 http://doi.org/10.1109/LGRS.2012.2191531 .

高放 , 刘宇 , 郭树旭 . 基于上下文窗口中反向搜索的高光谱图像无损压缩 . 光学精密工程 , 2015 . 23 ( 8 ): 2376 - 2383 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201508032 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201508032 .

F GAO , Y LIU , SH X GUO . Lossless compression of hyperspectral images using backward search and context window . Opt. Precision Eng. , 2015 . 23 ( 8 ): 2376 - 2383 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201508032 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201508032 .

M KLIMESH . Low-complexity lossless compression of hyperspectral imagery via adaptive filtering . IPN Progress Report , 2005 . 42 ( 163 ): 1 - 10 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0/fYmYzteVK3PNzLdYPRYEJ8688VbZ3Py/T9QqCoi6M= http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0/fYmYzteVK3PNzLdYPRYEJ8688VbZ3Py/T9QqCoi6M= .

D BASCONES , C GONZALEZ , D MOZOS . FPGA implementation of the CCSDS 1.2.3 standard for real-time hyperspectral lossless compression . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018 . 11 ( 4 ): 1158 - 1165 . DOI: 10.1109/JSTARS.2017.2767680 http://doi.org/10.1109/JSTARS.2017.2767680 .

CH CH LIN , Y TS HWANG . An efficient lossless compression scheme for hyperspectral images using two-stage prediction . IEEE Transactions on Geoscience & Remote Sensing Letters , 2010 . 7 ( 3 ): 558 - 562 .

李昌国 , 郭科 . 应用自适应预测器排序的三阶预测高光谱图像无损压缩 . 光学精密工程 , 2014 . 22 ( 3 ): 760 - 769 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201403032 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201403032 .

CH G LI , K GUO . Lossless compression of hyperspectral images using three-stage prediction based on adaptive predictor reordering . Opt. Precision Eng. , 2014 . 22 ( 3 ): 760 - 769 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201403032 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201403032 .

J W SONG , ZH W ZHANG , X M CHEN . Lossless compression of hyperspectral imagery via RLS filter . Electronics Letters , 2013 . 49 ( 16 ): 992 - 993 . DOI: 10.1049/el.2013.1315 http://doi.org/10.1049/el.2013.1315 .

J W SONG , L ZHOU , CH DENG , 等 . Lossless compression of hyperspectral imagery using a fast adaptive length prediction RLS filter . Remote Sensing Letters , 2019 . 10 ( 4 ): 401 - 410 . DOI: 10.1080/2150704X.2018.1562257 http://doi.org/10.1080/2150704X.2018.1562257 .

F GAO , SH X GUO . Lossless compression of hyperspectral images using conventional recursive least squares predictor with adaptive prediction bands . Journal of Applied Remote Sensing , 2016 . 10 ( 1 ): 1 - 9 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=465f0d5d23ec3b0e64b7e811e9e3a167 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=465f0d5d23ec3b0e64b7e811e9e3a167 .

高放 , 孙长建 , 邵庆龙 , 等 . 基于 K -均值聚类和传统递归最小二乘法的高光谱图像无损压缩 . 电子与信息学报 , 2016 . 38 ( 11 ): 2709 - 2714 . http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201611002 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201611002 .

F GAO , CH J SUN , Q L SHAO , 等 . Lossless compression of hyperspectral images using K -means Clustering and conventional recursive least-squares predictor . Journal of Electronics & Information Technology , 2016 . 38 ( 11 ): 2709 - 2714 . http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201611002 http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201611002 .

C KARACAA , M K GULLU . Lossless hyperspectral image compression using bimodal conventional recursive least-squares . Remote Sensing Letters , 2018 . 9 ( 1 ): 31 - 40 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1080/2150704X.2017.1375612 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1080/2150704X.2017.1375612 .

Views

591

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Lossless compression of hyperspectral images using backward search in context window

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

Hyperspectral Image Lossless Compression Based on Optimal Recursive Bidirection Prediction

Specral-spatial classification of hyperspectral imagery with hybrid architecture of 3D-CNN and Transformer

Collaborative classification of hyperspectral and LiDAR data based on CNN-transformer

Related Author

GUO Shu-xu

LIU Yu

GAO Fang

NIAN Yong-jian

XIN Qin

WAN Jian-wei

TANG Yi

孙蕾

Related Institution

College of Electronic Science and Engineering, Jilin University

College of Electronic Science and Engineering, National University of Defense Technology

湖南长沙国防科技大学理学院

Northwestern Polytechnical University

College of Electron and Information， University of Electronic Science and Technology of China，Zhongshan Institute

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰