An Efficient Lossless Compression Scheme for Hyperspectral Images Using Two-Stage Prediction

摘要

In this letter, an efficient lossless compression scheme for hyperspectral images is presented. The proposed scheme uses a two-stage predictor. The stage-1 predictor takes advantage of spatial data correlation and formulates the derivation of a spectral domain predictor as a process of Wiener filtering. The stage-2 predictor takes the prediction from the stage-1 predictor as an initial value and conducts a backward pixel search (BPS) scheme on the current band for the final prediction value. Experimental results show that the BPS scheme, aimed at exploiting calibration-induced data correlation, is effective on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 1997 images where such artifacts are significant. The proposed work outperforms all other schemes under comparison in this category. For the newer Consultative Committee for Space Data Systems images where calibration-induced artifacts are minimized, the BPS scheme does not help, and the stage-1 predictor alone achieves better compression performance.