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Lossless compression of large aperture static imaging spectrometer based on CCSDS-123
Author(s): Lu Yu; Xuebin Liu; Hongbo Li; Guizhong Liu
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Paper Abstract

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.

Paper Details

Date Published: 19 February 2018
PDF: 7 pages
Proc. SPIE 10607, MIPPR 2017: Multispectral Image Acquisition, Processing, and Analysis, 106070V (19 February 2018); doi: 10.1117/12.2288314
Show Author Affiliations
Lu Yu, Xi'an Institute of Optics and Precision Mechanics (China)
Xi'an Jiaotong Univ. (China)
Univ. of Chinese Academy of Sciences (China)
Xuebin Liu, Xi'an Institute of Optics and Precision Mechanics (China)
Hongbo Li, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Sciences (China)
Guizhong Liu, Xi'an Jiaotong Univ. (China)


Published in SPIE Proceedings Vol. 10607:
MIPPR 2017: Multispectral Image Acquisition, Processing, and Analysis
Xinyu Zhang; Jun Zhang; Hongshi Sang, Editor(s)

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