Lossless hyperspectral image compression via linear prediction
Jarno S. Mielikainen, Arto Kaarna, Pekka Toivanen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
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.