端元提取技术在高光谱图像压缩中的应用

高光谱图像海量数据如何实现大比例有效压缩是限制其应用的主要问题之一,而现有有损压缩方法存在大压缩比与光谱特性信息准确保留的矛盾,即使现有最优有损压缩方法也不能够得到令人满意的结果。文章基于混合像元分解的思想提出基于端元提取技术的数据有损压缩方法来解决该矛盾,首先用顶点成分分析(VCA)方法提取场景中地物的端元光谱,根据各端元与观测像元之间的光谱间余弦角相似性度量方法估计各端元的丰度,接着对端元光谱及丰度数据进行无损压缩,最后利用JPEG2000有损压缩方法对高光谱图像的所有单波段图像进行空间维大比例有损压缩。AVIRIS高光谱图像的仿真结果表明,压缩比得到大幅度提高,光谱信息得到有效恢复。在实现压缩比为50∶1时,大部分像元的光谱角误差在2%左右。

Endmember Extraction Used for Hyperspectral Imagery Loss Compression

One of the problems limiting the utility of hyperspectral imagery is how to compress the large number of data effectively. The current methods cannot resolve the problem of the contradiction between large compression rate and spectral information veracious reservation, even the best loss compression method can not bring the satisfying result. The paper presented a loss compression method based on the endmember extraction technology, so as to resolve the contradiction between large compression ratio and spectrum preserved accurately. The endmembers were obtained with vertex component analysis (VCA) and the fractions of them were estimated based on the proportion of cosine angle similitude between endmembers and observed spectrum. The endmembers spectrum and fraction were compressed with the lossless compression method and JPEG2000 loss compression method was used for all of the hyperspectral single-band images to increase compression ratio. The experiment on the AVIRIS data shows that compression ratio was increased greatly and the spectra were resumed effectively. When the compression ratio is 50 : 1, the spectrum angle loss is about 2% for most pixels.