Efficient compression of hyperspectral images by grouping around lines

被引：0

作者：

Gladkova, I. ^{[1
]}

Nalli, N. ^{[2
]}

Wolf, W. ^{[2
]}

Zhou, L. ^{[2
]}

Goldberg, M. ^{[3
]}

Roytman, L. ^{[1
]}

机构：

[1] CUNY City Coll, 138th St & Convent Ave, New York, NY 10031 USA

[2] QSS Group Inc, NOAA NESDIS, Camp Springs, MD 20746 USA

[3] ORA NOAA NESDI, E RA, Camp Springs, MD USA

来源：

INFORMATION OPTICS | 2006年 / 860卷

关键词：

compression; clustering; subspaces; least squares; hyperspectral infrared; satellite remote sensing; AIRS; GOES-R;

D O I：

暂无

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this paper we present a new lossless algorithm for compression of the signals from advanced hyperspectral infrared sensors onboard sun- and geo-synchronous environmental satellites. At each stage, our compression algorithm achieves an efficient grouping of channel data points around a relatively small number of I-dimensional lines in a large dimensional data space. The parametrization of these lines is very efficient, which leads to efficient descriptions of data points via adaptive clustering. Using one full day's worth (24 h) of global hyperspectral data obtained by the AQUA-EOS Atmospheric Infrared Sounder (AIRS), the mean ratio of compression attainable by the method is shown to be similar or equal to 3.7 to 1.

引用

页码：321 / +

页数：2

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