This paper presents a hyperspectral image lossless compression algorithm based on optimal recursive bidirection prediction. At first
different compression model for each band is chosen according to its spectral correlation factor. If the spectral correlation factor is less than 0.9
bzip2 compression model is chosen. Otherwise
single band optimal previous prediction is performed on the reference band and optimal recursive bidirection prediction is performed on the non-reference band and the residual images are coded by JPEG-LS. Our experimental findings are reported using the algorithms designed in this paper applied to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The result shows that the average compression ratio is 3.217
which is 0.09-1.374 higher than the other lossless compression algorithms.