Lossless Compression of Hyperspectral Imagery via Clustered Differential Pulse Code Modulation with Removal of Local Spectral Outliers

Author

Jiaji Wu ; Wanqiu Kong ; Mielikainen, Jarno ; Bormin Huang

Author_Institution

Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´an, China

Volume

22

Issue

12

fYear

2015

Firstpage

2194

Lastpage

2198

Abstract

A high-order clustered differential pulse code modulation method with removal of local spectral outliers (C-DPCM-RLSO) is proposed for the lossless compression of hyperspectral images. By adaptively removing the local spectral outliers, the C-DPCM-RLSO method improves the prediction accuracy of the high-order regression predictor and reduces the residuals between the predicted and the original images. The experiment on a set of the NASA Airborne Visible Infrared Imaging Spectrometer (AVIRIS) test images show that the C-DPCM-RLSO method has a comparable average compression gain but a much reduced execution time as compared with the previous lossless methods.

Keywords

hyperspectral imaging; infrared imaging; infrared spectrometers; pulse code modulation; regression analysis; AVIRIS test images; C-DPCM-RLSO method; NASA airborne visible infrared imaging spectrometer; high-order clustered differential pulse code modulation; high-order regression predictor; hyperspectral imagery; local spectral outlier removal; lossless compression; prediction accuracy; Accuracy; Hyperspectral imaging; Image coding; Modulation; NASA; Signal processing algorithms; C-DPCM; hyperspectral images; lossless compression; removal of local spectral outliers;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2015.2443913

Filename

7120937