Weighted-Correlation based Band Reordering Heuristics for Lossless Compression of Remote Sensing Hyperspectral Sounder Data

The redundancies in remote sensing hyperspectral sounder data give an ample opportunity to compress the images in various domains. A novel scheme of band reordering for the lossless compression of remote sensing hyperspectral sounder images is presented in this paper. The algorithm returns upon a well-organized technique for three-dimensional image band reordering. The presented band reordering method uses different weights on their inter-band correlation value for obtaining the optimal band ordering. The performances of the proposed band reordering method are tested using the Consultative Committee for Space Data Systems (CCSDS) 123 lossless predictor and three-dimensional context-based adaptive lossless image coding algorithms (3D-CALIC). The experimental results show the significant improvement on compression gain by using the proposed band ordering technique for different hyperspectral sounder data. The improvement is about 4-5% using 3D-CALIC and 6.5-7.5% using CCSDS.