Lossless Compression of Hyperspectral Images Using Multiband Lookup Tables

In this letter a novel method suitable for the lossless compression of hyperspectral imagery is presented. The proposed method generalizes two previous algorithms, in which the concept of nearest neighbor (NN) prediction implemented through either one or two lookup tables (LUTs) was introduced. Now M LUTs are defined on each of the N previous bands, from which prediction is calculated. The decision among one of the NmiddotM possible prediction values is based on the closeness of the values contained in the LUTs to an advanced prediction carried out from the values in the same N previous bands. Such a prediction is provided by either of two spectral predictors recently developed by the authors. Experimental results carried out on the AVIRIS'97 data set show improvements up to 18% over the baseline LUT-NN algorithm. However, preliminary results carried out on raw data show that all LUT-based methods are not suitable for on-board compression, since they take advantage uniquely of the data artifacts that may be originated by the on-ground calibration procedure.