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基于可逆整数变换的高光谱图像无损压缩

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罗欣 郭雷 杨诸胜

西北工业大学自动化学院,西安710072

光子学报
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国际标准刊号:ISSN 1004-4213
国内统一刊号:CN 61-1235

摘  要:

将变换矩阵分解为三角可逆矩阵(TERM)实现的整数Karhunen-Loève变换(IKLT),具有结构简单、完全可逆和同址运算的优点.将整数KLT和整数小波结合(IWT),提出了一种基于可逆整数变换的去相关方法:将KLT用于去除谱间冗余,并在对KLT的变换矩阵进行TERM分解的过程中,提出基于全局最大值选择主元的优化分解方法,保证了IKLT的准确度,同时明显降低了计算量;空间维的去相关变换采用基于提升结构的整数小波变换,同样保证了变换的完全可逆.采用不同编码策略,对不同场景的高光谱图像数据压缩的实验结果表明,基于整数混合变换的去相关方法能明显提高无损压缩比.[著者文摘]

文章出处:

《光子学报》-2007年36卷8期 -1457-1462页

Acta Photonica Sinica

栏目信息:

光谱学

分 类 号:

TP751.1

文献标识码:

A

文章编号:

1004-4213(2007)08-1457-6

相关文章:

参考文献(11篇) 耦合文献(14篇)  主题相关

[参考文献]

Lossless Compression of Hyperspectral Imagery with Reversible Integer Transform

LUO Xin,GUO Lei,YANG Zhu-sheng (College of Automation, Northwest Polytechnical University, Xilan 710072, China )

Abstract:

Hyperspectral imagery has very high spectral and spatial correlations. Since spectral information loss decreases the value of hyperspectral imagery for remote sensing applications, it is preferred to use lossless compression, although lossy compression can increase compression ratio. The Karhunen-Lo6ve Transform is theoretically the optimal transform to decorrelate hyperspectral data. However, since its transformed signal is real number, KLT is hardly applied in the field of lossless compression. The integer KLT (IKLT) based on triangular elementary reversible matrices(TERM) factorization of the transform matirx is perfectly reversible, and' can be computed in place. A lossless decorrelating algorithm for hyperspectral imagery compression combining the integer KLT and the integer wavelet transform (IWT) is proposed. A complete-maximum pivoting is used to constructed integer approximation of the KLT and leads to only limited error and more computational efficiency. In addition, given its promising performance in still image compresssion,an integer wavelet transform is implemented by the lifting scheme and adopted as spatial decorrelating transform,which is also inversible. The experimental results with different coding schemes and hyperspectral imagery from different scenes show that our decorrelating method can significantly enhance comprssion ratio.[著者文摘]

Key words:

Hyperspectral imagery; Lossless compression; Reversible integer mapping; Integer KLT; Lifting wavelet

收稿日期: 2006-05-22

基金资助:

国家自然科学基金(60175001)、西北工业大学研究生创业种子基金(Z200561)和国家863信息获取与处理主题资助

作者简介:

Tel.029—88474129Email:llxx028@tom.com LUO Xin was born in Sichuan Province,China,in 1977. She received the B. S. and the M. S. degree in electrical engineering from Northwest Polytechnical University, Xir an, China, in 1999 and 2004,respectively. She is currently pursing the Ph.D. degree in pattern recognition at Northwest Polytechnical University. Her current research interests include compression and processing of hyperspectral imagery,wavelet transform and pattern recognition.

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