Lossless hyperspectral image compression via linear prediction

Jarno S. Mielikainen; Arto Kaarna; Pekka J. Toivanen

doi:10.1117/12.478794

2 August 2002 Lossless hyperspectral image compression via linear prediction

Jarno S. Mielikainen, Arto Kaarna, Pekka J. Toivanen

Proceedings Volume 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII; (2002) https://doi.org/10.1117/12.478794
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

This paper proposes an interband version of the linear prediction approach for hyperspectral images. Linear prediction represents one of the best performing and most practical and general purpose lossless image compression techniques known today. The interband linear prediction method consists of two stages: predictive decorrelation producing residuals and entropy coding of the residuals. Our method achieved a compression ratio in the range of 3.02 to 3.14 using 13 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images.

Citation Download Citation

Jarno S. Mielikainen, Arto Kaarna, and Pekka J. Toivanen "Lossless hyperspectral image compression via linear prediction", Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002); https://doi.org/10.1117/12.478794

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