摘要
为了获得较高的压缩比,针对干涉超光谱图像数据的空间维相关性和干涉光谱维相关性,提出了一种将光谱分类与局部DPCM相结合的联合压缩算法。先对整个光谱数据进行光谱分类,得到一个与二维空间对应的分类号矩阵和一个与干涉光谱对应的光谱类别库,然后利用局部DPCM对光谱类别库进行进一步压缩。分类作为第一步压缩对整个压缩算法的压缩效果至关重要,本文分析了不同分类标准和分类精度下的压缩效果,相对欧氏距离标准优于夹角标准和干涉RQE标准。文中最后选取了合适的分类标准编程实现联合压缩算法并与JPEG2000进行比较,联合压缩算法的压缩效果优于JPEG2000。
In order to get a high compression ratio, according to the spatial dimension correlation and the interference spectral dimension correlation of interference hyperspectral image data, the present article provides a new compression algorithm that combines spectral classification with local DPCM. This algorithm requires spectral classification for the whole interference hyperspectral image to get a classification number matrix corresponding to the two-dimensional space and a spectral classification library corresponding to the interference spectra first, then local DPCM is performed for the spectral classification library to get a further compression. As the first step of the compression, the spectral classification is very important to the compression effect. This article analyzes the differences of compression effect with different standard and different accuracy of classification, the relative Euclidean distance standard is better than the angle standard and the interference RQE standard. Finally, this article chooses an appropriate standard of compression and achieves the combined compression algorithm with programming. Compared to JPEG2000, the compression effect of combined compression algorithm is better.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2013年第5期1401-1405,共5页
Spectroscopy and Spectral Analysis
基金
国家高技术研究发展计划项目(2011AA7012022)资助
关键词
干涉超光谱数据
数据压缩
光谱分类
DPCM
Interference hyperspectral data
Data compression
Spectral classification
DPCM
