Share Email Print
cover

Proceedings Paper

Clustered linear prediction for lossless compression of hyperspectral images using adaptive prediction length
Author(s): Jarno Mielikainen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

This paper extends clustered differential pulse code modulation (C-DPCM) lossless compression method for hyperspectral images. In C-DPCM method the spectra of a hyperspectral image is clustered, and an optimized predictor is calculated for each cluster. Prediction is performed using a linear predictor. After prediction, the difference between the predicted and original values is computed. The difference is entropy-coded using an adaptive entropy coder for each cluster. The proposed use of adaptive prediction length is shown have lower bits/pixel value than the original C-DPCM method for new AVIRIS test images. Both calibrated are uncalibrated images showed improvement over fixed prediction length.

Paper Details

Date Published: 24 August 2010
PDF: 7 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100M (24 August 2010); doi: 10.1117/12.863227
Show Author Affiliations
Jarno Mielikainen, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)

© SPIE. Terms of Use
Back to Top