基于降低映射预测残差的高光谱图像压缩算法

计算机工程与科学

基于降低映射预测残差的高光谱图像压缩算法

李佳颖1,2，朱文泉3，孟繁蕴1,2

(1.北京师范大学地理科学学部中药资源保护与利用北京市重点实验室，北京 100875；

2.北京师范大学地理科学学部天然药物教育部工程研究中心，北京 100875；

3.北京师范大学地理科学学部遥感科学与工程研究院，北京 100875)

收稿日期:2019-04-03 修回日期:2019-10-15 出版日期:2020-05-25 发布日期:2020-05-25
基金资助:
国家自然科学基金(81072999)

Hyperspectral image compression through

reducing mapping prediction residuals

LI Jia-ying1,2,ZHU Wen-quan3,MENG Fan-yun1,2

(1.Beijing Key Laboratory of Traditional Chinese Medicine Protection and Utilization,
Faculty of Geographical Science,Beijing Normal University,Beijing 100875;

2.Engineering Research Center of Natural Medicine,Ministry of Education,
Faculty of Geography Science,Beijing Normal University,Beijing 100875;

3.Institute of Remote Sensing Science and Engineering,
Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China)

Received:2019-04-03 Revised:2019-10-15 Online:2020-05-25 Published:2020-05-25

摘要/Abstract

摘要：

CCSDS 123.0-B-1算法是空间数据系统咨询委员会为多/高光谱图像提出的自适应三维预测无损压缩标准，针对CCSDS 123.0-B-1算法中存在的未充分利用像素位置信息及谱间相关性、压缩率有待提高的问题，对该算法的预测器进行了优化，提出了RMPR算法。RMPR算法根据当前像元具体位置对预测点进行自适应选择，采用双向线性预测去除高光谱图像的谱间相关性，并使用优化的残差映射器提高预测精度、缩短压缩码长。利用10幅高光谱图像进行测试，结果表明，在保证无损压缩且压缩效率无显著差异的前提下，RMPR算法的压缩性能显著优于原算法。

关键词: 高光谱图像, 无损压缩, CCSDS 123.0-B-1, 预测残差

Abstract:

CCSDS 123.0-B-1 algorithm is an adaptive lossless compression standard for multispectral and hyperspectral images proposed by the Consultative for Space Data Systems. In order to solve the problems existing in CCSDS 123.0-B-1 algorithm, such as the under-utilization of pixel position information and spectral correlation, and the compression rate to be improved, an RMPR (Reduction of Mapped Prediction Residual) algorithm is proposed to optimize the predictor of the algorithm. The RMPR algorithm can adaptively select the prediction points according to the specific position of the current pixel, remove the spectral correlation of hyperspectral images with bidirectional linear prediction, and use the optimized residual mapper to improve the prediction accuracy and shorten the compression code length. Test on ten hyperspectral images shows that the RMPR algorithm significantly outperforms the original algorithm in terms of compression performance, under the premise of lossless compression and no signi- ficant difference in compression efficiency.

Key words: hyperspectral image, lossless compression, CCSDS 123.0-B-1, prediction residual

李佳颖1,2，朱文泉3，孟繁蕴1,2. 基于降低映射预测残差的高光谱图像压缩算法[J]. 计算机工程与科学.

LI Jia-ying1,2,ZHU Wen-quan3,MENG Fan-yun1,2.

Hyperspectral image compression through

reducing mapping prediction residuals

[J]. Computer Engineering & Science.

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