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Proceedings Paper

Clustered DPCM with removing noise spectra for the lossless compression of hyperspectral images
Author(s): Jiaji Wu; Jianglei Xu
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Paper Abstract

The clustered DPCM (C-DPCM) lossless compression method by Jarno et al. for hyperspectral images achieved a good compression effect. It can be divided into three components: clustering, prediction, and coding. In the prediction part, it solves a multiple linear regression model for each of the clusters in every band. Without considering the effect of noise spectra, there is still room for improvement. This paper proposes a C-DPCM method with Removing Noise Spectra (C-DPCM-RNS) for the lossless compression of hyperspectral images.
C-DPCM-RNS's prediction part consists of two-times trainings. The prediction coefficients obtained from the first training will be used in the linear predictor to compute all the predicted values and then the difference between original and predicted values in current band of current class. Only the non-noise spectra are used in the second training. The resulting prediction coefficients from the second training will be used for prediction and sent to the decoder. The two-times trainings remove part of the interference of noise spectra, and reaches a better compression effect than other methods based on regression prediction.

Paper Details

Date Published: 26 October 2013
PDF: 4 pages
Proc. SPIE 8917, MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis, 891710 (26 October 2013); doi: 10.1117/12.2031461
Show Author Affiliations
Jiaji Wu, Xidian Univ. (China)
Jianglei Xu, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 8917:
MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis
Xinyu Zhang; Jianguo Liu, Editor(s)

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