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Lossless compression of large aperture static imaging spectrometer based on CCSDS-123
Yu, Lu1,2,3; Liu, Xuebin1; Li, Hongbo1,3; Liu, Guizhong2; Yu, Lu (yuluu921@163.com)
Conference Name10th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2017
Source PublicationMIPPR 2017: Multispectral Image Acquisition, Processing, and Analysis
Conference Date2017-10-28
Conference PlaceOctober 28, 2017 - October 29, 2017
Contribution Rank1

A new method for the lossless compression of the interferometer hyperspectral instrument Large Aperture Static Imaging Spectrometer (LASIS) data is presented in this paper. Differs from traditional hyperspectral instrument, the image captured by the two dimensional CCD detector of LASIS is no longer a normal image, but the two spatial information of the scene superimposed with interference fringes of equal thickness. There is a translation motion of the spatial information among each frame of LASIS data cube. Based on these unique data characteristics of LASIS and the recently presented CCSDS-123 lossless multispectral & Hyperspectral image compression standard, an improved predictor is designed for the prediction of LASIS data while using the standard. We perform several experiments on real data acquired by LASIS to investigate the performance of the proposed predictor. Experimental results show that the proposed predictor gives about 27.5% higher compression ratio than the default predictor of CCSDS-123 for lossless compression of LASIS data. In addition, the appropriate choice of several parameters of the proposed predictor are presented according to the experimental results. © 2018 SPIE.

Indexed ByEI
EI Accession Number20181204918349
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Document Type会议论文
Corresponding AuthorYu, Lu (yuluu921@163.com)
Affiliation1.Laboratory of Spectral Imaging Technique, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China
2.School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
3.University of Chinese Academy of Sciences, Beijing, 100049, China
Recommended Citation
GB/T 7714
Yu, Lu,Liu, Xuebin,Li, Hongbo,et al. Lossless compression of large aperture static imaging spectrometer based on CCSDS-123[C]:SPIE,2018.
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