Zhang Li-Yan, Chen De-Rong, Tao Peng
School of Aerospace Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Jul;28(7):1445-8.
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.
限制高光谱图像实用性的问题之一是如何有效压缩大量数据。当前方法无法解决高压缩率与光谱信息准确保留之间的矛盾问题，即使是最佳的有损压缩方法也不能带来令人满意的结果。本文提出了一种基于端元提取技术的有损压缩方法，以解决高压缩比与准确保留光谱之间的矛盾。通过顶点成分分析（VCA）获得端元，并基于端元与观测光谱之间的余弦角相似度比例估计它们的分数。端元光谱和分数采用无损压缩方法进行压缩，所有高光谱单波段图像使用JPEG2000有损压缩方法以提高压缩率。对AVIRIS数据的实验表明，压缩率大幅提高，光谱得到有效恢复。当压缩率为50:1时，大多数像素的光谱角损失约为2%。
